Advanced detection of sepsis

ABSTRACT

The present invention relates to methods, monitors and systems, useful, for example, for advanced detection of sepsis in a subject.

1. CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/203,367, filed Mar. 10, 2014, which is a continuation of U.S.application Ser. No. 12/935,727, filed Jan. 26, 2011, now U.S. Pat. No.8,669,113, which is a national stage of International Application No.PCT/US2009/002065, filed Apr. 2, 2009, which in turn claims benefit ofU.S. Provisional Application No. 61/123,071, filed Apr. 3, 2008. Thecontents of each of the above-cited applications are hereby incorporatedby reference herein in their entireties.

2. FIELD OF THE INVENTION

The present invention relates to methods, monitors and systems, useful,for example, for advanced detection of sepsis in a subject.

3. BACKGROUND OF THE INVENTION

Early detection of a disease condition typically allows for a moreeffective therapeutic treatment with a correspondingly more favorableclinical outcome. In many cases, however, early detection of diseasesymptoms is problematic due to the complexity of the disease; hence, adisease may become relatively advanced before diagnosis is possible.Systemic inflammatory conditions, such as sepsis, severe sepsis, septicshock, multiple organ dysfunction, represent one such class of diseases.These conditions, particularly sepsis, typically, but not always, resultfrom an interaction between a pathogenic microorganism and the host'sdefense system that triggers an excessive and dysregulated inflammatoryresponse in the host. The complexity of the host's response during theseptic response has complicated efforts towards understanding diseasepathogenesis (reviewed in Healy, 2002, Ann. Pharmacother. 36:648-54).Early and reliable diagnosis is imperative, however, because of theremarkably rapid progression of sepsis into a life-threateningcondition.

Sepsis follows a well-described time course, progressing from systemicinflammatory response syndrome (SIRS)-negative to SIRS-positive tosepsis, which may then progress to severe sepsis, septic shock, multipleorgan dysfunction (MOD), and ultimately death. Sepsis also may arise inan infected individual when the individual subsequently develops SIRS.“SIRS” is commonly defined as the presence of two or more of thefollowing parameters: body temperature greater than 38° C. or less than36° C.; heart rate greater than 90 beats per minute; respiratory rategreater than 20 breaths per minute; P_(CO2) less than 32 mm Hg; and awhite blood cell count either less than 4.0×10⁹ cells/L or greater than12.0×10⁹ cells/L, or having greater than 10% immature band forms.“Sepsis” is commonly defined as SIRS with a confirmed infectiousprocess. “Severe sepsis” is associated with MOD, hypotension,disseminated intravascular coagulation (DIC) or hypoperfusionabnormalities, including lactic acidosis, oliguria, and changes inmental status. “Septic shock” is commonly defined as sepsis-inducedhypotension that is resistant to fluid resuscitation with the additionalpresence of hypoperfusion abnormalities.

Documenting the presence of the pathogenic microorganisms that areclinically significant to sepsis has proven difficult. Causativemicroorganisms typically are detected by culturing a subject's blood,sputum, urine, wound secretion, in-dwelling line catheter surfaces, etc.Causative microorganisms, however, may reside only in certain bodymicroenvironments such that the particular material that is cultured maynot contain the contaminating microorganisms. Detection may becomplicated further by low numbers of microorganisms at the site ofinfection. Low numbers of pathogens in blood present a particularproblem for diagnosing sepsis by culturing blood. In one study, forexample, positive culture results were obtained in only 17% of subjectspresenting clinical manifestations of sepsis (Rangel-Frausto et al.,1995, JAMA 273:117-123). Diagnosis can be further complicated bycontamination of samples by non-pathogenic microorganisms. For example,only 12.4% of detected microorganisms were clinically significant in astudy of 707 subjects with septicemia (Weinstein et al., 1997, ClinicalInfectious Diseases 24:584-602).

The important of early diagnosis of sepsis is reflected by the highmorbidity and mortality associated with the disease. Sepsis currently isthe tenth leading cause of death in the United States and is especiallyprevalent among hospitalized subjects in non-coronary intensive careunits (ICUs), where it is the most common cause of death. The overallrate of mortality is as high as 35%, with an estimated 750,000 cases peryear occurring in the United States alone. The annual cost to treatsepsis in the United States alone is on the order of billions ofdollars.

Most existing sepsis scoring systems or predictive models predict onlythe risk of late-stage complications, including death, in subjects whoalready are considered septic and do not predict the development ofsepsis itself. Often, the diagnosis of sepsis is based on clinicalsuspicion with an empirical score system such as APACHE II (Knaus etal., 1985, Crit. Care Med. 13:818-829), followed by blood culture. Itcan take 48 hours or longer to confirm any systemic infections. By then,it is often too late to save some subjects. If detection of sepsis canbe made early, prior to manifestation of overt clinical symptomscurrently viewed as indicative of a clinically significant infection,treatment can be made available to prevent conversion of a SIRS-positivestate into sepsis, or to slow the conversion of sepsis into severesepsis or septic shock.

A need, therefore, exists for methods of the advanced detection ofsepsis using techniques that have satisfactory specificity andsensitivity, sufficiently early to allow effective intervention andprevention.

4. SUMMARY OF THE INVENTION

The present invention is based, in part, on the discovery that themeasurement of lysophosphatidylcholine in a sample from a subject can beused for rapid, sensitive and accurate advanced detection of sepsis in asubject. In aspects of the invention, lysophosphatidylcholine in asample from a subject, combined with one or more clinical markers and/orone or more biomarkers of the subject, is used to detect sepsis in thesubject. The methods, monitors and systems of the invention can be usedfor the advanced detection of any systemic inflammatory condition knownto those of skill in the art, including SIRS, sepsis, severe sepsis,septic shock and MOD.

In one aspect of the invention, a method for the advanced detection of asystemic inflammatory condition in a subject comprises the steps ofmeasuring lysophosphatidylcholine in fluid or tissue of the subject, andmeasuring one or more clinical markers of the subject, to detect thesystemic inflammatory condition in the subject. In certain embodiments,the method comprises the steps of measuring at a plurality of timepoints an amount of lysophosphatidylcholine in fluid or tissue of thesubject, and measuring at a plurality of time points one or moreclinical markers of the subject. In certain embodiments, the method isfor the advanced detection of sepsis. In certain embodiments, thesubject is SIRS-negative. In certain embodiments, the subject isSIRS-positive. In certain embodiments, the subject is SIRS-positive andsepsis-negative. In certain embodiments, the subject is sepsis-positive.

In certain embodiments, one clinical marker is measured.

In certain embodiments, temperature is measured.

In particular embodiments, respiratory rate is measured.

In certain embodiments, two or more clinical markers are measured.

In particular embodiments, respiratory rate and temperature aremeasured.

In certain embodiments, the lysophosphatidylcholine is1-O-acyl-2-lyso-sn-glycero-3-phosphocholine. The acyl group can be anyacyl group known to those of skill in the art. In certain embodiments,the acyl group is C₁₄-C₂₂ acyl. In further embodiments, the acyl groupis C₁₆-C₁₈ acyl. In particular embodiments, the acyl group is C₁₆ acyl.In a preferred embodiment, the acyl group is palmitoyl. In furtherparticular embodiments, the acyl group is C₁₈ acyl. In a preferredembodiment, the acyl group is stearoyl. The lysophosphatididylcholinecan be in any form from the subject, for instance any salt or solvatethat can be identified by those of skill in the art.

In a certain aspect, the present invention is based, in part, on thediscovery that lysophosphatidylcholine is useful for rapid, sensitiveand accurate advanced detection of a systemic inflammatory condition ina subject. As shown in the examples below, the methods, monitors andsystems of the invention can be used for the advanced detection ofsepsis in a subject with accuracy up to 58% or more. In particularembodiments, the advanced detection is at least 12 hours prior to theonset of sepsis.

In a certain aspect, the amount of lysophosphatidylcholine in a samplefrom a subject can be used for the advanced detection of sepsis in thesubject. The evaluation can proceed according to any technique forevaluating amount of lysophosphatidylcholine known to those of skill inthe art. Exemplary techniques are described herein. However, the presentinvention provides methods based on any technique of evaluatinglysophosphatidylcholine apparent to those of skill in the art.

In certain embodiments, measurements of the amount oflysophosphatidylcholine in the subject are made at a single time point(“snapshot”). In certain embodiments, measurements of the amount oflysophosphatidylcholine in the subject are made at a plurality of timepoints. In certain embodiments, measurement of the amount oflysophosphatidylcholine is made at regular time intervals. The timeintervals can be, for instance, 3 hours, 4 hours, 6 hours, 12 hours, 24hours or other intervals according to the judgment of the practitionerin the art. In these embodiments, the relative amounts oflysophosphatidylcholine are evaluated for the advanced detection ofsepsis in a subject by a practitioner of skill in the art. In particularembodiments, one amount of lysophophostidylcholine measured is 0, 12,24, 36 or 48 hours prior to the manifestation of overt clinical symptomscurrently viewed as indicative of a clinically significant infection. Inparticular embodiments, increasing amounts of lysophosphatidylcholineindicate decreased likelihood of onset of sepsis, and decreasing amountsof lysophosphatidylcholine detect sepsis or indicate increasedlikelihood of onset of sepsis.

In other embodiments, the evaluation is based on a comparison of theamount of lysophosphatidylcholine to a reference amount oflysophosphatidylcholine. The reference amount can be, for instance, theamount of lysophosphatidylcholine in a reference individual thatmanifests, or will manifest within a defined period of time, one or moresymptoms of the known systemic inflammatory condition. The amount canbe, for instance, an absolute value or an absolute value with a marginof error or a range of values, as determined by those of skill in theart. In certain embodiments, the reference individual exhibits, or willexhibit, symptoms of SIRS, sepsis, severe sepsis, septic shock, MOD, orno symptoms of a systemic inflammatory condition. In particularembodiments, low amounts of lysophosphatidylcholine (e.g. relative to areference amount) detect sepsis or indicate increased likelihood ofonset of sepsis, and high amounts of lysophosphatidylcholine (e.g.relative to a reference amount) indicate that sepsis is not detected orthat a reduced likelihood of onset of sepsis exists.

Advantageously, the reference amount need not be determined by onecarrying out a method of the invention. Instead, the reference amount oflysophosphatidylcholine can be identified by consulting data availableto those of skill in the art. Such data can be obtained from any sourceavailable to those of skill in the art. In certain embodiments, sourcescan be developed with reference amounts of lysophosphatidylcholinecollected by those of skill in the art according to methods describedherein.

In certain embodiments, the reference amount is from a referenceindividual presenting symptoms of the systemic inflammatory condition.The reference individual can present symptoms of SIRS, sepsis, severesepsis, septic shock, MOD, or no symptoms of a systemic inflammatorycondition. In certain embodiments, the reference amount can be evaluatedat a time prior to or after presentation of symptoms. For instance, inan advantageous embodiment, a reference amount can be the amountmeasured in a SIRS-positive individual 0, 12, 24, 36 or 48 hours priorto the manifestation of overt clinical symptoms currently viewed asindicative of a clinically significant infection. Measurement of suchreference amounts is within the skill of those in the art.

In further embodiments, reference amounts are from a plurality ofindividuals presenting symptoms of one or more systemic inflammatoryconditions. The reference amounts can be calculated according to anysuitable statistical method known to those of skill in the art. Forinstance, the reference amounts can be based on the statistical mean ofreference amounts from reference individuals presenting a systemicinflammatory condition. In advantageous embodiments, comparison is madeto a value or range of values for the amount of lysophosphatidylcholine.The value or range of values can be obtained as described herein andmade available to a practitioner of the methods of the invention. Inparticular embodiments, low amounts of lysophosphatidylcholine (e.g.relative to a reference amount) detect sepsis or indicate increasedlikelihood of onset of sepsis, and high amounts oflysophosphatidylcholine (e.g. relative to a reference amount) indicatethat sepsis is not detected or that a reduced likelihood of onset ofsepsis exists.

The comparison can be according to any technique for comparing amountsof lysophosphatidylcholine known to those of skill in the art. In oneembodiment, the advanced detection of sepsis is based on the differencebetween the amount of lysophosphatidylcholine in the subject and thereference amount. In certain embodiments, the difference between theamount of lysophosphatidylcholine in the subject and the referenceamount correlates inversely with detection of sepsis or with anincreased likelihood of onset of sepsis. In further embodiments, thereference amount is a threshold—in other words, amounts oflysophosphatidylcholine less than the threshold amount detect sepsis orindicate increased likelihood of onset of sepsis. Such thresholdreference amounts can be calculated according to methods describedherein.

The amount of lysophosphatidylcholine in the subject can be determinedaccording to any technique known to those of skill in the art withoutlimitation. It is known that lysophosphatidylcholine is bound toproteins such as albumin in the circulation (J. Joles et. al. KidneyInternational Vol. 56, Suppl. 71 (1999), pp S57-61). In certainembodiments, one of skill can measure an amount that correlates to theamount of lysophosphatidylcholine in a sample. For instance, inparticular embodiments, one of skill can measure freelysophosphatidylcholine, bound lysophosphatidylcholine or free and boundlysophosphatidylcholine in the sample to indicate the amount of totallysophosphatidylcholine in the sample. In other words, in certainembodiments, measurement of free or bound, or both free and bound,lysophosphatidylcholine can correlate to the amount of totallysophosphatidylcholine. In certain embodiments, the technique forevaluating amount of lysophosphatidylcholine is not critical for theinvention and need not be carried out by one practicing the methodsherein. For instance, in particular embodiments, methods of theinvention can comprise the single step of comparinglysophosphatidylcholine amount in a subject to a referencelysophosphatidylcholine amount in order to detect sepsis or indicatelikelihood of onset of sepsis without regard to how either amount ismeasured.

In further embodiments, the amount of lysophosphatidylcholine in thesubject is evaluated by a technique described herein followed bycomparing to a reference amount of lysophosphatidylcholine in order todetect sepsis or indicate likelihood of onset of sepsis. In certainembodiments, the amount of lysophosphatidylcholine is evaluated byspectrometry, chromatography, immunoassay, electrophoresis,electrochemical method or enzymatic assay as described in detail below.

The amount of lysophosphatidylcholine can be measured in fluids ortissues of the subject as provided herein. Processes for preparing thefluid or tissue, for example, processes for extracting or purifyinglysophosphatidylcholine are described herein. Further, techniques formeasuring lysophosphatidylcholine are provided herein. In certainembodiments, the fluid or tissue of the subject is blood, plasma,saliva, serum, sputum, urine, cells, cellular extract or tissue biopsy.

In another aspect of the invention, a method for the advanced detectionof a systemic inflammatory condition in a subject comprises the steps ofmeasuring at a plurality of time points a compound according to formula(I):

or a salt or solvate thereof, in fluid or tissue of the subject, andmeasuring at a plurality of time points one or more clinical markers ofthe subject to detect the systemic inflammatory condition in thesubject. In certain embodiments, the method is for the advanceddetection of sepsis.

In certain embodiments, one clinical marker is measured.

In certain embodiments, temperature is measured.

In particular embodiments, respiratory rate is measured.

In certain embodiments, two or more clinical markers are measured.

In particular embodiments, respiratory rate and temperature aremeasured.

In certain embodiments, the subject is SIRS-negative. In certainembodiments, the subject is SIRS-positive. In certain embodiments, thesubject is SIRS-positive and sepsis-negative. In certain embodiments,the subject is sepsis-positive.

In formula (I), R can be any acyl group known to those of skill in theart. In certain embodiments, R is saturated acyl. In furtherembodiments, R is C₁₀-C₂₂ acyl. In further embodiments, R is C₁₄-C₂₂acyl. In further embodiments, R is C₁₆-C₂₀ acyl. In further embodiments,R is C₁₆-C₁₈ acyl. In particular embodiments, R is C₁₆ saturated acyl.In particular embodiments, R is C₁₈ saturated acyl. In particularembodiments, R is unbranched C₁₆-C₁₈ acyl. In a preferred embodiment, Ris palmitoyl. In a preferred embodiment, R is stearoyl.

Exemplary salts of formula (I) are provided by formula (Ia):

wherein said salt can be coordinated with any physiological organic orinorganic anion, or any physiological organic or inorganic cation, orboth, known to those of skill in the art. Exemplary physiological anionsinclude chloride, bromide, phosphate, acetate, carbonate, bicarbonateand sulfate. Exemplary physiological cations include sodium, potassium,calcium, magnesium and ammonium.

In certain embodiments, the advanced detection is 0, 12, 24, 36 or 48hours prior to the onset of sepsis.

In certain embodiments, advanced detection may immediately prior to theonset of sepsis, for example, 1 hour, 30 minutes, 15 minutes, or even 1minute prior to the onset of sepsis.

In certain embodiments, one amount of the compound according to Formula(I) measured is 0, 12, 24, 36 or 48 hours prior to the onset of sepsis.

In certain embodiments, the difference between the amounts measured forthe compound according to Formula (I) correlates inversely withdetection of sepsis or with an increased likelihood of onset of sepsis.

In certain embodiments of the invention, the method for the advanceddetection of sepsis in a subject further comprises measuring at aplurality of time points one or more biomarkers in fluid or tissue ofthe subject.

In certain embodiments, a difference in the amount of one or morebiomarkers measured at a plurality of time points detects sepsis orindicates increased likelihood of onset of sepsis.

In certain embodiments, the amount of one or more biomarkers measured ata plurality of time points correlates inversely with detection of sepsisor with an increased likelihood of onset of sepsis.

In certain embodiments, the amount of one or more biomarkers measured ata plurality of time points correlates directly with detection of sepsisor with an increased likelihood of onset of sepsis.

In certain embodiments, one or more biomarkers are selected from thegroup consisting of endotoxin, bacterial DNA, protein C, protein S,procalcitonin (PCT), C-reactive protein (CRP), LBP LPS-binding protein,fibrin degrading products, HLA-DR, cell surface proteins CD-14 andCD-64, E-selectin, cortisol, ACTH, surface-bound tumor necrosis factorreceptor I (sTNFRI), surface-bound tumor necrosis factor receptor II(sTNF-RII), TNF-α, interleukins IL-6, IL-8 and IL-10, D-dimer,prothrombin, antithrombin III, activated partial thromboplastin,plasminogen activator inhibitor-1, soluble thrombomodulin, thrombinactivatable fibrinolysis inhibitor, copeptin, high mobility group box 1(HMGB1), triggering receptor expressed on myeloid cells 1 (TREM1) andalbumin.

In particular embodiments, one or more biomarkers compriseprocalcitonin.

In certain embodiments, the amount of the biomarker is measured byspectrometry, chromatography, immunoassay, electrophoresis or enzymaticassay.

The amount of biomarker can be measured in fluids or tissues of thesubject as provided herein. Processes for preparing the fluid or tissue,for example, processes for extracting or purifying the biomarker aredescribed herein. Further, techniques for measuring the biomarker areprovided herein. In certain embodiments, the fluid or tissue of thesubject is blood, plasma, saliva, serum, sputum, urine, cells, cellularextract or tissue biopsy.

In another aspect of the invention, a method for the advanced detectionof a systemic inflammatory condition in a subject comprises the steps ofmeasuring at a plurality of time points the amount of procalcitonin influid or tissue of the subject, and measuring at a plurality of timepoints one or more clinical markers of the subject. In certainembodiments, the method is for the advanced detection of sepsis. Incertain embodiments, the subject is SIRS-negative. In certainembodiments, the subject is SIRS-positive. In certain embodiments, thesubject is SIRS-positive and sepsis-negative. In certain embodiments,the subject is sepsis-positive.

In certain embodiments, the one or more clinical markers are selectedfrom the group consisting of respiratory rate, temperature, heart rate,systolic blood pressure, diastolic blood pressure mean artery pressure,white blood cell count, monocyte count, lymphocyte count, granulocytecount, neutrophil count, immature neutrophil to total neutrophil ratio,platelet count, serum creatinine concentration, urea concentration,lactate concentration, glucose concentration, base excess, pO₂ and HCO₃⁻ concentration.

In certain embodiments, one clinical marker is measured.

In certain embodiments, temperature is measured.

In particular embodiments, respiratory rate is measured.

In certain embodiments, two or more clinical markers are measured.

In particular embodiments, respiratory rate and temperature aremeasured.

In certain embodiments, the advanced detection is 0, 12, 24, 36 or 48hours prior to the onset of sepsis.

In certain embodiments, a body temperature greater than 38° C. detectssepsis.

In certain embodiments, a body temperature less than 36° C. detectssepsis.

In certain embodiments, a respiratory rate greater than 20 breaths perminute detects sepsis.

In another aspect, the present invention provides monitors for theadvanced detection of a systemic inflammatory condition. In suchmonitors, measurement of the amount of lysophosphatidylcholine is usedto monitor for the systemic inflammatory condition in the subject. Insuch monitors, changes in the amount of lysophosphatidylcholine arecapable of indicating changes in the systemic inflammatory condition.For instance, in certain embodiments, decreasing amounts oflysophosphatidylcholine detect sepsis or indicate increased likelihoodof onset of sepsis, and increasing amounts of lysophosphatidylcholineindicate that sepsis is not detected or that a reduced likelihood ofonset of sepsis exists.

In some embodiments, amount of lysophosphatidylcholine can indicateconversion from one systemic inflammatory condition to another or nosystemic inflammatory condition. In a particularly advantageousembodiment, amount of lysophosphatidylcholine can indicate conversionfrom the SIRS-positive state to septic state.

In certain embodiments, provided herein are modules that detectlysophosphatidylcholine.

In certain embodiments, the invention provides monitors for the advanceddetection of sepsis in a subject comprising: (a) a sensor module capableof measuring one or more clinical markers of the subject, and (b) achemistry module capable of measuring an amount oflysophosphatidylcholine in fluid or tissues of the subject. In certainembodiments, the sensor module is capable of measuring at a plurality oftime points one or more clinical markers of the subject. In certainembodiments, the chemistry module is capable of measuring at a pluralityof time points an amount of lysophosphatidylcholine in fluid or tissuesof the subject. In particularly advantageous embodiments, the monitor iscapable of continuous measuring. In certain embodiments, the monitor iscapable of measuring two or more markers. In particular embodiments, themonitor is capable of measuring respiratory rate and temperature.

In certain embodiments, the invention provides monitors for the advanceddetection of sepsis in a subject comprising: (a) a sensor module thatmeasures one or more clinical markers of the subject, and (b) achemistry module that measures an amount of lysophosphatidylcholine influid or tissues of the subject. In certain embodiments, the sensormodule measures at a plurality of time points one or more clinicalmarkers of the subject. In certain embodiments, the chemistry modulemeasures at a plurality of time points an amount oflysophosphatidylcholine in fluid or tissues of the subject. Inparticularly advantageous embodiments, the monitor continuouslymeasures. In certain embodiments, the monitor measures two or moremarkers. In particular embodiments, the monitor measures respiratoryrate and temperature.

In certain embodiments, the chemistry module is capable of contacting asample from the fluid or tissue of the subject with one or more reagentscapable of generating a fluorescent product indicative of amount oflysophosphatidylcholine in the sample. In certain embodiments, thechemistry module is capable of detecting the presence oflysophosphatidylcholine or of detecting the amount oflysophosphatidylcholine, or both, in the sample. In particularembodiments, the chemistry module is capable of contacting the samplewith a fluorogenic substrate of one or more of the reagents. Thisfluorogenic substrate can be converted to the fluorescent productindicating amount of lysophosphatidylcholine. In advantageousembodiments, the reagents comprise peroxidase, choline oxidase,glycerophosphatidylcholine diesterase and lysophospholipase. Anexemplary fluorogenic substrate is 10-acetyl-3,7-dihydroxyphenoxazine, acompound that can be converted to the fluorescent product7-hydroxy-3H-phenoxazin-3-one.

In certain embodiments, the sample from the fluid or tissue of thesubject in the chemistry module is contacted with one or more reagentscapable of generating a fluorescent product indicative of amount oflysophosphatidylcholine in the sample. In certain embodiments, thechemistry module detects the presence of lysophosphatidylcholine or ofdetecting the amount of lysophosphatidylcholine, or both, in the sample.In particular embodiments, the chemistry module contacts the sample witha fluorogenic substrate of one or more of the reagents. This fluorogenicsubstrate can be converted to the fluorescent product indicating amountof lysophosphatidylcholine. In advantageous embodiments, the reagentscomprise peroxidase, choline oxidase, glycerophosphatidylcholinediesterase and lysophospholipase. An exemplary fluorogenic substrate is10-acetyl-3,7-dihydroxyphenoxazine, a compound that can be converted tothe fluorescent product 7-hydroxy-3H-phenoxazin-3-one.

In certain embodiments, the invention provides monitors for the advanceddetection of sepsis in a subject comprising: (a) a first sensor modulecapable of measuring the subject's temperature, (b) a second sensormodule capable of measuring the subject's respiratory rate, and (c) achemistry module capable of measuring an amount oflysophosphatidylcholine in fluid or tissues of the subject. In certainembodiments, the first and second sensor modules are capable ofmeasuring at a plurality of time points the subject's temperature andthe subject's respiratory rate. In certain embodiments, the chemistrymodule is capable of measuring at a plurality of time points an amountof lysophosphatidylcholine in fluid or tissues of the subject. Inparticularly advantageous embodiments, the monitor is capable ofcontinuous measuring.

In certain embodiments, the invention provides monitors for the advanceddetection of sepsis in a subject comprising: (a) a first sensor modulethat measures the subject's temperature, (b) a second sensor module thatmeasures the subject's respiratory rate, and (c) a chemistry module thatmeasures an amount of lysophosphatidylcholine in fluid or tissues of thesubject. In certain embodiments, the first and second sensor modulesmeasure at a plurality of time points the subject's temperature and thesubject's respiratory rate. In certain embodiments, the chemistry modulemeasures at a plurality of time points an amount oflysophosphatidylcholine in fluid or tissues of the subject. Inparticularly advantageous embodiments, the measurement at a plurality oftime points is continuous.

In certain embodiments, the first sensor module is capable of connectingto a temperature probe in the subject. In particular embodiments, thetemperature probe is capable of being applied externally to thesubject's skin. In particular embodiments, the temperature probe iscapable of being applied internally to the subject. In particularembodiments, the temperature probe is capable of being applied to thesubject's esophagus or rectum.

In certain embodiments, the first sensor module is connected to atemperature probe in the subject. In particular embodiments, thetemperature probe is applied externally to the subject's skin. Inparticular embodiments, the temperature probe is applied internally tothe subject. In particular embodiments, the temperature probe is appliedto the subject's esophagus or rectum.

In certain embodiments, the second sensor module is capable ofconnecting to a motion sensor. In particular embodiments, the motionsensor is a flow meter. In particular embodiments, the flow meter is ina respirator.

In certain embodiments, the second sensor module is connected to amotion sensor. In particular embodiments, the motion sensor is a flowmeter. In particular embodiments, the flow meter is in a respirator.

In certain embodiments, the individual sensor modules and chemistrymodules of the monitor operate separately of one another.

In certain embodiments, the invention provides for a stand-alone monitorcapable of measuring lysophosphatidylcholine in fluid or tissues of thesubject.

In certain embodiments, the invention provides for a stand-alone monitorcapable of measuring at a plurality of time points an amount oflysophosphatidylcholine in fluid or tissues of the subject.

In certain embodiments, the invention provides for a stand-alone monitorthat measures lysophosphatidylcholine in fluid or tissues of thesubject.

In certain embodiments, the invention provides for a stand-alone monitorthat measures at a plurality of time points an amount oflysophosphatidylcholine in fluid or tissues of the subject.

In another aspect, the present invention provides systems for theadvanced detection of a systemic inflammatory condition. Such systemscomprise a computational device capable of combining the clinical markerand lysophosphatidylcholine measurements obtained from the monitors ofthe invention into a result indicative of status of the subject. Incertain embodiments, a value less than a reference threshold value,specified for a particular systemic inflammatory condition, indicatesthat the condition is not detected or that a decreased likelihood ofonset of the condition exists, and a value greater than the referencethreshold value detects the condition or indicates increased likelihoodof onset of the condition. For instance, in certain embodiments, a valueless than a reference threshold value for sepsis indicates that sepsisis not detected or that a decreased likelihood of onset of sepsisexists, and a value greater than the reference threshold value forsepsis detects sepsis or indicates increased likelihood of onset ofsepsis. In other embodiments, a “no” value indicates that sepsis is notdetected or that a reduced likelihood of onset of sepsis exists, and a“yes” value indicates that sepsis is detected or an increased likelihoodof onset of sepsis.

In certain aspects, the invention provides systems for the advanceddetection of sepsis in a subject comprising: (a) one or more monitors ofthe invention, (b) a computational device capable of combining theclinical marker and lysophosphatidylcholine measurements obtained fromthe monitors into a result, comprising: (i) a device capable ofreceiving the clinical marker and lysophosphatidylcholine measurementsfrom the one or more monitors; (ii) a microprocessor with an algorithmcapable of combining the measurements into a result; (iii) a devicecapable of transmitting the result to a module capable of storing,displaying and/or transmitting, and (c) a module capable of storing,displaying and/or transmitting the result.

In certain aspects, the invention provides systems for the advanceddetection of sepsis in a subject comprising: (a) one or more monitors ofthe invention, (b) a computational device that combines the clinicalmarker and lysophosphatidylcholine measurements obtained from themonitors into a result, comprising: (i) a device that receives theclinical marker and lysophosphatidylcholine measurements from the one ormore monitors; (ii) a microprocessor with an algorithm that combines themeasurements into a result; (iii) a device that transmits the result toa module that stores, displays and/or transmits, and (c) a module thatstores, displays and/or transmits the result.

In certain aspects, the invention provides systems for the advanceddetection of sepsis in a subject comprising: (a) one or more monitors ofthe invention, (b) a computational device capable of combining thetemperature, respiratory rate, and lysophosphatidylcholine measurementsobtained from the monitors into a result, comprising: (i) a devicecapable of receiving temperature, respiratory rate, andlysophosphatidylcholine measurements from the one or more monitors; (ii)a microprocessor with an algorithm capable of combining the measurementsinto a result; (iii) a device capable of transmitting the result to amodule capable of storing, displaying and/or transmitting, and (c) amodule capable of storing, displaying and/or transmitting the result.

In certain aspects, the invention provides systems for the advanceddetection of sepsis in a subject comprising: (a) one or more monitors ofthe invention, (b) a computational device that combines the temperature,respiratory rate, and lysophosphatidylcholine measurements obtained fromthe monitors into a result, comprising: (i) a device that receivestemperature, respiratory rate, and lysophosphatidylcholine measurementsfrom the one or more monitors; (ii) a microprocessor with an algorithmthat combines the measurements into a result; (iii) a device thattransmits the result to a module that stores, displays and/or transmits,and (c) a module that stores, displays and/or transmits the result.

In certain aspects, the invention provides systems for the advanceddetection of sepsis in a subject comprising: (a) a sensor module capableof measuring at a plurality of time points one or more clinical markersof the subject, (b) a chemistry module capable of measuring at aplurality of time points an amount of lysophosphatidylcholine orprocalcitonin in fluid or tissue of the subject, (c) a computationaldevice capable of combining the measurements in (a) and the measurementsin (b) into a result, comprising: (i) a device capable of receiving themeasurements in (a) and the measurements in (b) from the one or moremodules; (ii) a microprocessor with an algorithm capable of combiningthe measurements into a result; and (iii) a device capable oftransmitting the result to a module capable of storing, displayingand/or transmitting; and (d) a module capable of storing, displaying ortransmitting the result.

In certain aspects, the invention provides systems for the advanceddetection of sepsis in a subject comprising: (a) a sensor module thatmeasures at a plurality of time points one or more clinical markers ofthe subject, (b) a chemistry module that measures at a plurality of timepoints an amount of lysophosphatidylcholine or procalcitonin in fluid ortissue of the subject, (c) a computational device that combines themeasurements in (a) and the measurements in (b) into a result,comprising: (i) a device that receives the measurements in (a) and themeasurements in (b) from the one or more modules; (ii) a microprocessorwith an algorithm that combines the measurements into a result; and(iii) a device that transmits the result to a module that stores,displays and/or transmits; and (d) a module that stores, displays ortransmits the result.

In certain embodiments of the invention, the module capable of storing,displaying and/or transmitting is a display module.

In certain embodiments of the invention, the device in (i) and themicroprocessor in (ii) are the same device.

In certain embodiments, the algorithm of the microprocessor is selectedfrom discriminant analysis, quadratic discriminant analysis, logicalregression analysis, regression classifiers, neural networks, andcombinations thereof.

In certain embodiments, the computational device is further capable ofcomparing the lysophosphatidylcholine amount to a reference amountindicative of the amounts of lysophosphatidylcholine in fluids ortissues of a plurality of individuals that have, or will have, sepsis.

In certain embodiments, the computational device compares thelysophosphatidylcholine amount to a reference amount indicative of theamounts of lysophosphatidylcholine in fluids or tissues of a pluralityof individuals that have, or will have, sepsis.

In particular embodiments, the reference amount is the amount measuredin a SIRS-positive individual 0, 12, 24, 36 or 48 hours prior to theonset of sepsis.

In certain embodiments, the result is a number that detects sepsis.

In certain embodiments, the result is a “yes/no” signal, wherein “yes”detects sepsis.

In certain embodiments, the result is displayed on a screen.

In certain embodiments, the result is transmitted to the medical recordof the subject.

5. BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 provides an embodiment of the systems of the invention;

FIG. 2 is a graphical representation of ROC curves for historicallysophosphatidylcholine, temperature and respiratory rate data;

FIG. 3 is a graphical representation of ROC curves for testlysophosphatidylcholine, temperature and respiratory rate data;

FIG. 4 is a graphical representation of the time of call for testlysophosphatidylcholine, temperature and respiratory rate data;

FIG. 5 is a graphical representation of ROC curves for historicallysophosphatidylcholine, temperature, respiratory rate, and biomarkerdata;

FIG. 6 is a graphical representation of ROC curves for testlysophosphatidylcholine, temperature, respiratory rate, and biomarkerdata;

FIG. 7 is a graphical representation of the time of call for testlysophosphatidylcholine, temperature, respiratory rate, and biomarkerdata;

FIG. 8 is a graphical representation of daily marker values andlogit(P(Sepsis)) values calculated for a SIRS test patient and a sepsistest patient;

FIG. 9 is a graphical representation of the probability (P(Sepsis))values calculated for a SIRS test patient and a sepsis test patient.

6. DETAILED DESCRIPTION OF THE INVENTION 6.1 Definitions

As used herein, the following terms shall have the following meanings:

The term “subject” refers to animals such as mammals, including, but notlimited to, primates (e.g., humans), cows, sheep, goats, horses, dogs,cats, rabbits, rats, mice and the like. In preferred embodiments, thesubject is human. The term “subject” is interchangeable with a humansubject, unless noted otherwise.

“Systemic inflammatory response syndrome,” or “SIRS,” refers to aclinical response to a variety of severe clinical insults, as manifestedby two or more of the following conditions within a 24-hour period:

-   -   body temperature greater than 38° C. (100.4° F.) or less than        36° C. (96.8° F.);    -   heart rate (HR) greater than 90 beats/minute;    -   respiratory rate (RR) greater than 20 breaths/minute, or P_(CO2)        less than 32 mmHg, or requiring mechanical ventilation; and    -   white blood cell count (WBC) either greater than 12.0×10⁹/L or        less than 4.0×10⁹/L or having greater than 10% immature band        forms.

These symptoms of SIRS represent a consensus definition of SIRS that canbe modified or supplanted by other definitions in the future. Thepresent definition is used to clarify current clinical practice and doesnot represent a critical aspect of the invention (see, e.g., AmericanCollege of Chest Physicians/Society of Critical Care Medicine ConsensusConference: Definitions for Sepsis and Organ Failure and Guidelines forthe Use of Innovative Therapies in Sepsis, 1992, Crit. Care. Med. 20,864-874, the entire contents of which are herein incorporated byreference).

A subject with SIRS has a clinical presentation that is classified asSIRS, as defined above, but is not clinically deemed to be septic.Methods for determining which subjects are at risk of developing sepsisare well known to those in the art. Such subjects include, for example,those in an intensive care unit (ICU) and those who have otherwisesuffered from a physiological trauma, such as a burn, surgery or otherinsult. A hallmark of SIRS is the creation of a proinflammatory statethat can be marked by tachycardia, tachypnea or hyperpnea, hypotension,hypoperfusion, oliguria, leukocytosis or leukopenia, pyrexia orhypothermia and the need for volume infusion. SIRS characteristicallydoes not include a documented source of infection (e.g., bacteremia).

“Sepsis” refers to a systemic host response to infection with SIRS plusa documented infection (e.g., a subsequent laboratory confirmation of aclinically significant infection such as a positive culture for anorganism). Thus, sepsis refers to the systemic inflammatory response toa documented infection (see, e.g., American College of Chest PhysiciansSociety of Critical Care Medicine, Chest, 1997, 101:1644-1655, theentire contents of which are herein incorporated by reference). As usedherein, “sepsis” includes all stages of sepsis including, but notlimited to, the conversion to sepsis, severe sepsis, septic shock andmultiple organ dysfunction (“MOD”) associated with the end stages ofsepsis.

The “onset of sepsis” refers to an early stage of sepsis, e.g., prior toa stage when conventional clinical manifestations are sufficient tosupport a clinical suspicion of sepsis. Because the methods of thepresent invention can be used to detect sepsis prior to a time thatsepsis would be suspected using conventional techniques, in certainembodiments, the subject's disease status at early sepsis is confirmedretrospectively, when the manifestation of sepsis is more clinicallyobvious. The exact mechanism by which a subject becomes septic is not acritical aspect of the invention. The methods of the present inventioncan detect the onset of sepsis independent of the origin of theinfectious process.

“Advanced detection of sepsis” refers to detection of sepsis, orlikelihood of development of clinical manifestations sufficient tosupport a clinical suspicion of sepsis, that is, prior to the onset ofovert signs indicative of a clinically significant infection, prior tothe development of such clinical manifestations. In certain embodiments,advanced detection of sepsis means at least 48 hours prior to thedevelopment of such clinical manifestations. In other embodiments,advanced detection of sepsis means at least 36 hours prior to thedevelopment of such clinical manifestations. In other embodiments,advanced detection of sepsis means at least 24 hours prior to thedevelopment of such clinical manifestations. In other embodiments,advanced detection of sepsis means at least 12 hours prior to thedevelopment of such clinical manifestations. In other embodiments,advanced detection of sepsis means at least 6 hours prior to thedevelopment of such clinical manifestations. In other embodiments,advanced detection of sepsis means at least 3 hours prior to thedevelopment of such clinical manifestations. In other embodiments,advanced detection of sepsis means at least 1 hour prior to thedevelopment of such clinical manifestations. In other embodiments,advanced detection of sepsis means just prior to the development of suchclinical manifestations determined by an attending physician.

“Severe sepsis” refers to sepsis associated with organ dysfunction,hypoperfusion abnormalities, or sepsis-induced hypotension.Hypoperfusion abnormalities include, but are not limited to, lacticacidosis, oliguria, or an acute alteration in mental status.

“Septic shock” refers to sepsis-induced hypotension that is notresponsive to adequate intravenous fluid challenge and withmanifestations of peripheral hypoperfusion.

A “clinical marker” refers to a physiological parameter that can bemeasured in the subject, such as a clinical vital sign. Examplesinclude, but are not limited to respiratory rate, temperature, heartrate, systolic blood pressure, diastolic blood pressure mean arterypressure, white blood cell count, monocyte count, lymphocyte count,granulocyte count, neutrophil count, immature neutrophil to totalneutrophil ratio, platelet count, serum creatinine concentration, ureaconcentration, lactate concentration, glucose concentration, baseexcess, pO₂ and HCO₃ ⁻ concentration.

A “converter” or “converter subject” refers to a SIRS-positive subjectwho progresses to clinical suspicion of sepsis during the period thesubject is monitored, typically during an ICU stay.

A “non-converter” or “non-converter subject” refers to a SIRS-positivesubject who does not progress to clinical suspicion of sepsis during theperiod the subject is monitored, typically during an ICU stay.

A “biomarker” is a compound that is present in or derived from abiological sample. “Derived from” as used in this context refers to acompound that, when detected, is indicative of a particular moleculebeing present in the biological sample. For example, detection of aparticular fragment of a compound can be indicative of the presence ofthe compound itself in the biological sample. A biomarker can, forexample, be isolated from the biological sample, directly measured inthe biological sample, or detected in or determined to be in thebiological sample. A biomarker can, for example, be functional,partially functional, or non-functional.

As used herein, “conventional techniques” in the context of the advanceddetection of a systemic inflammatory condition are those techniques thatclassify a subject based on phenotypic changes without evaluating abiomarker according to the present invention.

As used herein, the term “specifically,” and analogous terms, in thecontext of an antibody, refers to peptides, polypeptides, and antibodiesor fragments thereof that specifically bind to an antigen or a class ofantigens, or fragments thereof, and do not specifically bind to otherantigens or other fragments. A peptide or polypeptide that specificallybinds to an antigen may bind to other peptides or polypeptides withlower affinity, as determined by standard experimental techniques, forexample, by any immunoassay well-known to those skilled in the art. Suchimmunoassays include, but are not limited to, radioimmunoassays (RIAs)and enzyme-linked immunosorbent assays (ELISAs). Antibodies or fragmentsthat specifically bind to an antigen may be cross-reactive with relatedantigens. Preferably, antibodies or fragments thereof that specificallybind to an antigen do not cross-react with other antigens. See, e.g.,Paul, ed., 2003, Fundamental Immunology, 5th ed., Raven Press, New Yorkat pages 69-105, which is incorporated by reference herein, for adiscussion regarding antigen-antibody interactions, specificity andcross-reactivity, and methods for determining all of the above.

As used herein, a “reference population” is a population of subjectsthat can be used to construct an algorithm for evaluation of a biomarkerof subjects at risk for developing a systemic inflammatory condition.

A “reference subject” is a subject that has been diagnosed, or will bediagnosed within a defined period of time, with a systemic inflammatorycondition according to standards recognized by those of skill in theart. A reference subject is useful for establishing a reference amountof the biomarker that can be used to evaluate an amount of the biomarkerin a test subject for the advanced detection of a systemic inflammatorycondition.

As used herein, a “training population” is a set of samples from apopulation of subjects used to fit an algorithm. In a preferredembodiment, a training population includes samples from subjects thatare converters and subjects that are nonconverters.

As used herein, an “algorithm” is a statistical model used to combinelysophosphatidylcholine, clinical and/or biomarker values into a resultthat detects or is indicative of sepsis. Representative algorithms aredescribed in Section 5.12.

As used herein, “ROC” means a receiver operator characteristic curvethat can be used to evaluate algorithm performance.

As used herein, a “functional” is a function whose input is a functionor curve, and whose output is a number.

The term “label” refers to a display of written, printed or graphicmatter upon the immediate container of an article, for example thewritten material displayed on a vial containing a pharmaceuticallyactive agent.

The term “labeling” refers to all labels and other written, printed orgraphic matter upon any article or any of its containers or wrappers oraccompanying such article, for example, a package insert orinstructional audios or videos, e.g. videotapes or DVDs, accompanying orassociated with a container of a pharmaceutically active agent.

“Acyl” refers to a radical —C(O)R, where R is alkyl.

“Alkyl,” by itself or as part of another substituent, means, unlessotherwise stated, a straight or branched chain hydrocarbon radical whichmay be fully saturated, mono- or polyunsaturated, having the number ofcarbon atoms designated (i.e., C₁-C₂₂ means one to twenty-two carbons).Examples of saturated hydrocarbon radicals include groups such asmethyl, ethyl, n-propyl, isopropyl, n-butyl, t-butyl, isobutyl,sec-butyl, cyclohexyl, (cyclohexyl)methyl, cyclopropylmethyl, homologsand isomers of, for example, n-pentyl, n-hexyl, n-heptyl, n-octyl, andthe like. An unsaturated alkyl group is one having one or more doublebonds or triple bonds. Examples of unsaturated alkyl groups includevinyl, 2-propenyl, crotyl, 2-isopentenyl, 2-(butadienyl),2,4-pentadienyl, 3-(1,4-pentadienyl), ethynyl, 1- and 3-propynyl,3-butynyl, and the higher homologs and isomers.

“Physiologically acceptable salt” refers to a salt of a compound of theinvention that is pharmaceutically acceptable and that possesses thedesired pharmacological activity of the parent compound. Such saltsinclude: (1) acid addition salts formed with organic or inorganic acidssuch as hydrochloric, hydrobromic, sulfuric, nitric, phosphoric, acetic,trifluoroacetic, trichloroacetic, propionic, hexanoic,cyclopentylpropionic, glycolic, glutaric, pyruvic, lactic, malonic,succinic, sorbic, ascorbic, malic, maleic, fumaric, tartaric, citric,benzoic, 3-(4-hydroxybenzoyl)benzoic, picric, cinnamic, mandelic,phthalic, lauric, methanesulfonic, ethanesulfonic,1,2-ethane-disulfonic, 2-hydroxyethanesulfonic, benzenesulfonic,4-chlorobenzenesulfonic, 2-naphthalenesulfonic, 4-toluenesulfonic,camphoric, camphorsulfonic,4-methylbicyclo[2.2.2]-oct-2-ene-1-carboxylic, glucoheptonic,3-phenylpropionic, trimethylacetic, tert-butylacetic, lauryl sulfuric,gluconic, benzoic, glutamic, hydroxynaphthoic, salicylic, stearic,muconic acid and the like acids; or (2) salts formed when an acidicproton present in the parent compound either (a) is replaced by a metalion, e.g., an alkali metal ion, an alkaline earth ion or an aluminumion, or alkali metal or alkaline earth metal hydroxides, such as sodium,potassium, calcium, magnesium, and barium hydroxide, ammonia or (b)coordinates with an organic base, such as aliphatic, alicyclic, oraromatic organic amines, such as methylamine, dimethylamine,diethylamine, picoline, ethanolamine, diethanolamine, triethanolamine,N-methylglucamine and the like.

Salts further include, by way of example only, sodium, potassium,calcium, magnesium, ammonium, tetraalkylammonium and the like, and whenthe compound contains a basic functionality, salts of non-toxic organicor inorganic acids, such as hydrochloride, hydrobromide, tartrate,mesylate, acetate, maleate, oxalate and the like. The term“physiologically acceptable cation” refers to a non-toxic,physiologically acceptable cationic counterion of an acidic functionalgroup. Such cations are exemplified by sodium, potassium, calcium,magnesium, ammonium and tetraalkylammonium cations and the like.

“Solvate” refers to a compound of the present invention or a saltthereof, that further includes a stoichiometric or non-stoichiometricamount of solvent bound by non-covalent intermolecular forces. Where thesolvent is water, the solvate is a hydrate.

It is to be understood that compounds having the same molecular formulabut differing in the nature or sequence of bonding of their atoms or inthe arrangement of their atoms in space are termed “isomers”. Isomersthat differ in the arrangement of their atoms in space are termed“stereoisomers”.

6.2 Embodiments of the Invention

The present invention allows, for example, for the rapid and accurateadvanced detection of sepsis in a subject by measuring the amount oflysophosphatidylcholine in a sample from a subject, combined with one ormore clinical markers and/or one or more biomarkers of the subject.Amounts of lysophosphatidylcholine can be constructed from one or morebiological samples of subjects at a single time point (“snapshot”), ormultiple such time points, during the course of time the subject is atrisk for sepsis. Advantageously, sepsis can be diagnosed or predictedprior to the onset of conventional clinical symptoms, thereby allowingfor more effective therapeutic intervention.

6.2.1. Subjects

In certain embodiments of the invention, the subject is an animal,preferably a mammal, more preferably a non-human primate. In the mostpreferred embodiments, the subject is a human.

Although the methods of the invention can be used for the advanceddetection of any sepsis-related disease, particularly useful subjectsinclude those that are at risk for sepsis. The subject can be at riskfor sepsis according to any criteria known to the practitioner of skillin the art.

In certain embodiments, the subject is SIRS-negative. In the context ofthis invention, SIRS-negative subjects include healthy subjects that,for any reason according to the judgment of a practitioner of the art,are in need of advanced detection of sepsis. Such subjects include, butare not limited to, SIRS-negative subjects in hospital intensive careunits and similarly situated subjects. The methods of the invention canbe used for the advanced detection of SIRS, sepsis, severe sepsis,septic shock, multiple organ dysfunction or mortality. Further methodsof the invention can be used to monitor for increased or decreasedlikelihood of onset of SIRS, sepsis, severe sepsis, septic shock,multiple organ dysfunction or mortality. In particular embodiments, thesubject is a subject that might be at risk for a systemic inflammatorycondition, such as a subject of an intensive care unit.

In further embodiments, the subject is SIRS-positive. The methods of theinvention can be used for the advanced detection of sepsis, severesepsis, septic shock, multiple organ dysfunction or mortality, or todetect conversion to SIRS-negative. Further methods of the invention canbe used to monitor a treatment or prevention for increased or decreasedlikelihood of onset of sepsis, severe sepsis, septic shock, multipleorgan dysfunction or mortality, or to monitor for possible conversion toSIRS-negative.

In further embodiments, the subject has sepsis. The methods of theinvention can be used for the advanced detection of severe sepsis,septic shock, multiple organ dysfunction or mortality, or to detectconversion to SIRS-positive (and sepsis-negative) or SIRS-negative.Further methods of the invention can be used to monitor a treatment orprevention for increased or decreased likelihood of onset of severesepsis, septic shock, multiple organ dysfunction or mortality, or tomonitor for possible conversion to SIRS-positive (and sepsis-negative)or SIRS-negative.

In further embodiments, the subject has severe sepsis. The methods ofthe invention can be used for the advanced detection of septic shock,multiple organ dysfunction or mortality, or to detect conversion tosepsis, SIRS-positive (and sepsis-negative) or SIRS-negative. Furthermethods of the invention can be used to monitor a treatment orprevention for increased or decreased likelihood of onset of septicshock, multiple organ dysfunction or mortality, or to monitor forpossible conversion to sepsis, SIRS-positive (and sepsis-negative) orSIRS-negative.

In further embodiments, the subject has septic shock. The methods of theinvention can be used for the advanced detection of multiple organdysfunction or mortality, or to detect conversion to severe sepsis,sepsis, SIRS-positive (and sepsis-negative) or SIRS-negative. Furthermethods of the invention can be used to monitor a treatment orprevention for increased or decreased likelihood of onset of multipleorgan dysfunction or mortality, or to monitor for possible conversion tosevere sepsis, sepsis, SIRS-positive (and sepsis-negative) orSIRS-negative.

In further embodiments, the subject has multiple organ dysfunction. Themethods of the invention can be used for the advanced detection ofmortality, or they can be used to detect conversion to septic shock,severe sepsis, sepsis, SIRS-positive (and sepsis-negative) orSIRS-negative. Further methods of the invention can be used to monitor atreatment or prevention for increased or decreased likelihood of onsetof mortality, or to monitor for possible conversion to septic shock,severe sepsis, sepsis, SIRS-positive (and sepsis-negative) orSIRS-negative.

In preferred embodiments, the subject is SIRS-negative (i.e. the subjectcan be healthy) but in need of diagnosis or prognosis of a systemicinflammatory condition according to the judgment of a practitioner ofskill in the art. The subject could be, for instance, a patient in anintensive care unit. Similarly, in preferred embodiments, subjects areSIRS-negative, and methods of the invention are used to assesslikelihood of the onset of a systemic inflammatory condition. Forinstance, in a SIRS-negative subject judged to be at risk for a systemicinflammatory condition, for example according to a method of theinvention, a course of intervention could be administered to the subjectto prevent a systemic inflammatory condition. Such prevention of asystemic inflammatory condition can be monitored with a method of theinvention.

In further preferred embodiments, subjects are SIRS-positive, andmethods of the invention are used for the advanced detection of afurther systemic inflammatory condition. Similarly, in preferredembodiments, subjects are SIRS-positive, and methods of the inventionare used to monitor treatment of SIRS or prevention of the furthersystemic inflammatory condition. For instance, in a SIRS-positivesubject judged to be at risk for a further systemic inflammatorycondition, for example according to a method of the invention, a courseof intervention could be administered to the subject to prevent thesystemic inflammatory condition. Such prevention of the systemicinflammatory condition can be monitored with a method of the invention.

In specific embodiments, a subject is monitored using the methods,monitors and systems of the invention as frequently as necessary (e.g.,during their stay in an intensive care unit) for the advanced detectionof a systemic inflammatory condition. In a preferred embodiment, thesubject is monitored soon after they arrive in an intensive care unit.In some embodiments, the subject is monitored daily after they arrive inan intensive care unit. In some embodiments, the subject is monitoredevery 1 to 3 hours, 3 to 8 hours, 8 to 12 hours, 12 to 16 hours, or 16to 24 hours after they arrive in an intensive care unit. In someembodiments, the subject is monitored continuously after they arrive inan intensive care unit.

6.3 Lysophosphatidylcholine

In one aspect, the present invention provides for the advanced detectionof sepsis in a subject based on measurement of an amount oflysophosphatidylcholine in fluid or tissue of the subject. In certainembodiments, measurement of lysophosphatidylcholine is made at aplurality of time points.

In certain embodiments, the amount of total lysophosphatidylcholine in asample from a subject is used for the advanced detection of sepsis.Total lysophosphatidylcholine refers to an amount that corresponds toall lysophosphatidylcholine (free or bound or both) in the sample. Forinstance, total lysophosphatidylcholine can refer to those molecules inthe sample that are according to formula (I):

or any salt or solvate thereof, wherein R is any acyl group. The acylgroup can be any acyl group known to those of skill in the art.Exemplary acyl groups include caproyl, lauroyl, myristoyl, palmitoyl,stearoyl, palmitoleyl, oleyl, arachidonyl and linoleyl. Preferably,total lysophosphatidylcholine includes at least1-O-palmitoyl-2-lyso-sn-glycero-3-phosphocholine and1-O-stearoyl-2-lyso-sn-glycero-3-phosphocholine. In typical embodiments,amount of lysophosphatidylcholine is measured without regard to theidentity of the acyl group. Useful techniques are described herein.

In certain embodiments, the amount of lysophosphatidylcholine measuredat a plurality of time points and one or more clinical markers measuredat a plurality of time points are used for the advanced detection ofsepsis. In certain embodiments, the clinical markers are temperature andrespiratory rate.

In further embodiments, one or more biomarkers are additionally used forthe advanced detection of sepsis.

The practitioner of skill in the art can use any technique to measure orindicate amount of lysophosphatidylcholine in a sample. In certainembodiments, the practitioner of skill can measure an amount or valuefrom a sample that correlates to total lysophosphatidylcholine. Forinstance, in certain samples from subjects, a fraction of totallysophosphatidylcholine can be free from other molecules while a furtherfraction of total lysophosphatidylcholine can be bound by othermolecules. For example, a fraction of total lysophosphatidylcholine canbe bound by albumin. The sample preparation and measurement techniquesused by the practitioner of skill can affect the amount oflysophosphatidylcholine actually measured. For instance, precipitationand/or purification techniques can separate free and boundlysophosphatidylcholine. Detection techniques might be more sensitive tofree lysophosphatidylcholine or to bound lysophosphatidylcholine. Thisamount measured can be correlated to the amount of totallysophosphatidylcholine in the sample according to methods available tothe practitioner of skill. In certain embodiments, a measurement of freelysophosphatidylcholine is used to indicate the amount of totallysophosphatidylcholine in the sample. In certain embodiments, ameasurement of bound lysophosphatidylcholine is used to indicate theamount of total lysophosphatidylcholine in the sample. In certainembodiments, a measurement of bound and free lysophosphatidylcholine isused to indicate the amount of total lysophosphatidylcholine in thesample. In certain embodiments, free lysophosphatidylcholine can be usedfor the advanced detection of sepsis in the methods of the invention. Incertain embodiments, bound lysophosphatidylcholine can be used for theadvanced detection of sepsis in the methods of the invention. In certainembodiments, free and bound lysophosphatidylcholine can be used for theadvanced detection of sepsis in the methods of the invention.

In another aspect, the present invention provides for the advanceddetection of sepsis in a subject based on measurement an amount of acompound according to formula (I):

or any salt or solvate thereof, wherein R is an acyl group, in fluid ortissue of the subject.

Exemplary salts of formula (I) are provided by formula (Ia):

wherein said salt can be coordinated with any ion or ions known to thoseof skill in the art. The ion or ions can be physiological but need notbe physiological. For instance, the ion or ion can result from contactwith the salt during the preparation of the sample from the subject, asdescribed below. In some embodiments, the salt is coordinated with ananion, for instance, a physiological anion known to those of skill inthe art. Exemplary anions include chloride, bromide, phosphate, acetate,carbonate, bicarbonate and sulfate. In some embodiments, the salt iscoordinated with a cation, for instance, a physiological cation, knownto those of skill in the art. Exemplary cations include sodium,potassium, calcium, magnesium and ammonium. In some embodiments, as willbe recognized by those of skill in the art, the salt is coordinated withone or more anions and with one or more cations.

The acyl group can be any acyl group known to those of skill in the art.In certain embodiments the acyl group is saturated. Exemplary saturatedacyl groups include caproyl, lauroyl, myristoyl, palmitoyl and stearoyl.In further embodiments, the acyl group is monounsaturated. Exemplarymonounsaturated acyl groups include palmitoleyl and oleyl. In furtherembodiments, the acyl group is polyunsaturated. Exemplarypolyunsaturated acyl groups include arachidonyl and linoleyl.

More systematically, in certain embodiments, the acyl group is C₁₀-C₂₂acyl. In certain embodiments, the acyl group is C₁₄-C₂₂ acyl. Exemplaryacyl groups include 16:0, 18:0, 18:1, 18:2, 20:4(n-6) and 22:6(n-3),according to nomenclature familiar to those of skill in the art. In suchnomenclature, the first number indicates the number of carbon atoms inthe acyl group, and the second number indicates the number of doublebonds in the group. For instance, “18:1” indicates an acyl group with 18carbon atoms and one double bond. Numbers in parentheses, if any,indicate the location of the double bond, and the notation “(n-x)”indicates a double bond x positions away from the terminal methyl of thelongest chain of the fatty acid. See Biochem. J., 1978, 171, 21-35;Chem. Phys. Lipids, 1978, 21, 159-173; Eur. J. Biochem., 1977, 79,11-21; Hoppe-Seyler's Z. Physiol. Chem., 1977, 358, 617-631; J. LipidRes., 1978, 19, 114-128; Lipids, 1977, 12, 455-468; Mol. Cell. Biochem.,1977, 17, 157-171; Biochemical Nomenclature and Related Documents, 2ndedition, Portland Press, 1992, pages 180-190, the contents of which arehereby incorporated by reference in their entireties.

In certain embodiments, the acyl group is C₁₄-C₂₂ acyl. In certainembodiments, the acyl group is C₁₆-C₉₀ acyl. In further embodiments, theacyl group is C₁₆-C₁₈ acyl. In certain embodiments, the acyl group ishexadecanoyl or octadecanoyl. In particular embodiments, the acyl groupis C₁₆ acyl. In a preferred embodiment, the acyl group is hexadecanoyl.In further particular embodiments, the acyl group is C₁₈ acyl. In apreferred embodiment, the acyl group is octadecanoyl.

The compound according to formula (I) can be any form of the compoundfrom the subject, for instance any salt or solvate of the compound thatcan be identified by those of skill in the art. In preferredembodiments, the compound is in the form of a sodium salt.

In certain embodiments, the amounts of the compound according to formula(I) may include a precursor of the compound known to those of skill inthe art. The precursor can be one or two or three, or in someembodiments more, steps prior to the compound according to formula (I)in a biosynthetic pathway known to those of skill in the art. In furtherembodiments, the amounts of the compound according to formula (I) mayinclude a downstream metabolite of the compound in a biosyntheticpathway known to those of skill in the art. The downstream metabolitecan be one or two or three, or in some embodiments more, steps followingthe compound according to formula (I) in a biosynthetic pathway known tothose of skill in the art. In certain embodiments, the biosyntheticpathway is a de novo pathway for the synthesis of platelet activatingfactor known to those of skill in the art. In further embodiments, thebiosynthetic pathway is a remodeling pathway for the synthesis ofplatelet activating factor known to those of skill in the art.

In particular embodiments, the metabolite is a1-O-acyl-2-O-acyl-sn-glycero-3-phosphocholine. In preferred embodiments,the 2-O-acyl group is any acyl group described above or acetyl. Inparticular embodiments, the metabolite is a1-O-acyl-2-O-alkyl-sn-glycero-3-phosphocholine. In preferredembodiments, the 2-O-alkyl group is any group known to those of skill inthe art to modify a glycero-3-phosphocholine, for instance any C₁₄-C₂₂alkyl.

6.4 Clinical Markers

In another aspect, the present invention provides for the advanceddetection of sepsis in a subject based on measurement at a plurality oftime points of one or more clinical markers of the subject. In certainembodiments, the clinical markers are measured in conjunction withmeasurements of the amounts of lysophosphatidylcholine or the compoundof formula (I). The clinical markers may be measured at the same timethe measurements of the amounts of lysophosphatidylcholine or thecompound of formula (I) are made, for example, simultaneously with theblood draws for measurement of the amounts. The clinical markers may bemeasured at a plurality of time points than those at which themeasurements of the amounts of lysophosphatidylcholine or the compoundof formula (I) are made, for example, up to several hours before orafter the blood draws for measurement of the amounts. The clinicalmarkers may be measured on the same day as the measurements of theamounts of lysophosphatidylcholine or the compound of formula (I) aremade, for example, within 24 hours of the blood draws for measurement ofthe amounts. The clinical markers may be measured as a maximum orminimum value over the 24 hours preceding measurement of the amounts oflysophosphatidylcholine or the compound of formula (I) are made (this isthe Apache II measurement).

The clinical marker for the advanced detection of sepsis can be anyclinical marker for a systemic inflammatory condition, including SIRS,sepsis, severe sepsis, septic shock or MOD, known to those of skill inthe art. In certain embodiments, the clinical marker is according to aclinical severity model for sepsis. Such models include, but are notlimited to, the Acute Physiology and Chronic Health Evaluation score(APACHE, and its refinements APACHE II and III) (Knaus et al., 1985,Crit Care Med 13: 818-829; Knaus et al., 1991, Chest 100: 1619-1636),the Mortality Prediction Model (MPM) (Lemeshow et al., 1993, JAMA 270:2957-2963), the Simplified Acute Physiology (SAPS) score (Le Gall etal., 1984, Crit Care Med 12: 975-977), the Multiple Organ DysfunctionScore (MODS) (Marshall et al., 1995, Crit Care Med 23: 1638-1652), theSequential Organ Failure Assessment (SOFA) score (Ferreira et al., 2002,JAMA 286: 1754-1758), the Logistical Organ Dysfunction Score (LODS) (LeGall et al., 1996, JAMA 276: 802-810) and the predisposition, infection,response, and organ dysfunction (PIRO) concept (Levy et al., 2003,Intensive Care Med 29: 530-538) (the contents of each reference ishereby incorporated in its entirety).

In certain embodiments, the clinical markers comprise one or moremeasurements used by those of skill in the art to aid in the prognosisor diagnosis of sepsis. Such markers include, but are not limited to,temperature, heart rate, systolic blood pressure, diastolic bloodpressure, mean artery pressure, white blood cell count, differentialwhite blood cell count (monocytes, lymphocytes, granulocytes and/orneutrophils), immature neutrophil to total neutrophil ratio, plateletcount and serum creatinine.

In preferred embodiments, the clinical markers are respiratory rate andtemperature.

In certain embodiments, a body temperature greater than 38° C. detectssepsis. In certain embodiments, a body temperature greater than 38.5° C.detects sepsis. In certain embodiments, a body temperature greater than39° C. detects sepsis. In certain embodiments, a body temperaturegreater than 39.5° C. detects sepsis. In certain embodiments, a bodytemperature greater than 40° C. detects sepsis.

In certain embodiments, a body temperature less than 36° C. detectssepsis. In certain embodiments, a body temperature less than 35.5° C.detects sepsis. In certain embodiments, a body temperature less than 35°C. detects sepsis. In certain embodiments, a body temperature less than34.5° C. detects sepsis. In certain embodiments, a body temperature lessthan 34° C. detects sepsis.

Temperature can be measured by any technique deemed useful by one ofskill in the art. Exemplary techniques are described herein includingthe monitors and systems described below.

In certain embodiments, a respiratory rate greater than 20 breaths perminute detects sepsis. In certain embodiments, a respiratory rategreater than 21 breaths per minute detects sepsis. In certainembodiments, a respiratory rate greater than 22 breaths per minutedetects sepsis. In certain embodiments, a respiratory rate greater than23 breaths per minute detects sepsis. In certain embodiments, arespiratory rate greater than 24 breaths per minute detects sepsis. Incertain embodiments, a respiratory rate greater than 25 breaths perminute detects sepsis.

Respiratory rate can be measured by any technique deemed useful by oneof skill in the art. Exemplary techniques are described herein includingthe monitors and systems described below.

6.5 Biomarkers

In another aspect, the present invention provides for the advanceddetection of sepsis in a subject based additionally on measurement at aplurality of time points of one or more biomarkers of the subject. Incertain embodiments, the biomarkers are measured in conjunction with themeasurements at a plurality of time points of clinical markers, and/orin conjunction with measurements of the amounts oflysophosphatidylcholine or the compound of formula (I). The biomarkersmay be measured at the same time the measurements of the amounts oflysophosphatidylcholine or the compound of formula (I) are made, forexample, from the same blood draw used for measurement oflysophosphatidylcholine. The biomarkers may be measured at a pluralityof time points than those at which the measurements of the amounts oflysophosphatidylcholine or the compound of formula (I) are made, forexample, from different blood draws taken hours apart. The biomarkersmay also be measured at a plurality of time points than those at whichthe clinical marker measurements are made, for example, up to severalhours before or after the clinical marker measurement.

Each biomarker can be of any type of biomarker for a systemicinflammatory condition known to those of skill in the art includingprotein, peptide, nucleic acid, lipid, phospholipid and metabolite(e.g., protein, peptide, nucleic acid, nucleoside, lipid or phospholipidmetabolite) biomarkers. Further exemplary biomarkers for the prognosisor diagnosis of a systemic inflammatory condition, and methods of theirevaluation, are described in U.S. Patent Application Publication Nos.20030194752, 20040096917, 20040097460, 20040106142, 20040157242, andU.S. Provisional Application Nos. 60/671,620, filed Apr. 15, 2005,60/671,941, filed Apr. 15, 2005, and 60/674,046, filed Apr. 22, 2005,the contents of which are hereby incorporated by reference in theirentireties. Further exemplary biomarkers for sepsis include endotoxin;bacterial DNA; acute phase proteins such as protein C, procalcitonin,LBP-LPS-binding protein; coagulation factors such as fibrin degradingproducts, antithrombin III, dimer D; membrane cell markers such asHLA-DR, CD-64, E-selectin; hormones such as cortisol, ACTH; solublereceptors such as CD-14, sTNFRI, sTNF-RII; and cytokines such as TNF,IL-6, IL-8 and IL-10; and others such as D-dimer, prothrombin time,activated partial thromboplastin time, plasminogen activatorinhibitor-1, soluble thrombomodulin, IL-6, IL-10, IL-8, protein C,thrombin activatable fibrinolysis inhibitor, protein S, antithrombin,TNF-α, copeptin, high mobility group box 1, triggering receptorexpressed on myeloid cells 1, and albumin. See, e.g., Kinasewitz et al.,2004, Critical Care 8:R82-R90, Bozza et al., 2005, Mem. Inst. OswaldoCruz 100(s)1:217-221, the contents of which are hereby incorporated byreference in their entireties. Preferred biomarkers includeprocalcitonin. The sequence of the procalcitonin protein, for example,the human procalcitonin protein, is well known to those of skill in theart. An exemplary procalcitonin protein can be identified, for example,by accession number P01258, (UniProtKB/Swiss-Prot) by one of skill inthe art.

In one embodiment, any of the biomarkers are human.

6.6 Measurement of Lysophosphatidylcholine

In this section and the sections that follow, unless specifiedotherwise, the term lysophosphatidylcholine refers to an amount oflysophosphatidylcholine in fluid or tissue of a subject or to an amountof the compound according to formula (I), its salt or solvate thereof,wherein R is C₁₀-C₂₂ acyl, in the fluid or tissue of a subject.

In certain embodiments of the invention, the method of measuringlysophosphatidylcholine is not critical. Accordingly, the presentinvention provides methods for the advance detection of sepsis thatcomprise the step of detecting sepsis from measuring at a plurality oftime points an amount of lysophosphatidylcholine, for example, alongwith one or more clinical markers and/or one or more biomarkers, asdescribed above.

The amount of lysophosphatidylcholine can be measured by one practicinga method of the invention in any manner whatsoever. Exemplary techniquesare described herein. As described above, any technique that indicateslysophosphatidylcholine in the sample can be used in the methods of theinvention. In certain embodiments, the methods are based on freelysophosphatidylcholine in the sample. In certain embodiments, themethods are based on bound lysophosphatidylcholine in the sample. Incertain embodiments, the methods are based on totallysophosphatidylcholine in the sample.

The amount of a lysophosphatidylcholine can be measured by onepracticing a method of the invention in any manner whatsoever. Exemplarytechniques are described herein.

When an amount of lysophosphatidylcholine is to be evaluated, eachlysophosphatidylcholine in the amount should be evaluated according to atechnique suitable for that lysophosphatidylcholine. For example, theamount of 1-O-palmitoyl-2-lyso-sn-glycero-3-phosphocholine should beevaluated according to a technique suitable for1-O-palmitoyl-2-lyso-sn-glycero-3-phosphocholine. In advantageousembodiments, lysophosphatidylcholines within the amount that can beevaluated by the same or by compatible techniques can be evaluatedtogether. In other advantageous embodiments lysophosphatidylcholines andadditional biomarkers that can be evaluated by the same or by compatibletechniques can be evaluated together. For instance, protein, peptide,lipid, phospholipid and metabolite biomarkers that can be evaluated byimmunoassays can be evaluated together or in groups according totechniques known to those of skill in the art.

In one embodiment, only a single biological sample taken at a singlepoint in time from the subject is used to detect sepsis prior toconversion. In another embodiment, a plurality of biological samplestaken at different points in time from the subject are used to detectsepsis prior to conversion.

In a specific embodiment, the amount of lysophosphatidylcholine isobtained using samples collected from the subject at one time point. Inanother specific embodiment, the amount of lysophosphatidylcholine isobtained using samples obtained from the subject at separate timepoints. In one example, these samples are obtained from the subjecteither once or, alternatively, on a daily basis, or more frequently,e.g., every 2, 3, 4, 6, 8 or 12 hours.

Lysophosphatidylcholine can be obtained from any biological sample,which can be, by way of example and not of limitation, blood, plasma,serum, saliva, sputum, urine, cerebral spinal fluid, cells, a cellularextract, a tissue sample, a tissue biopsy, a stool sample or any samplethat may be obtained from a subject using techniques well known to thoseof skill in the art. The precise biological sample that is taken fromthe subject may vary, but the sampling preferably is minimally invasiveand is easily performed by conventional techniques.

In advantageous embodiments, the amount of lysophosphatidylcholine canbe detected, measured or monitored by one or more enzymatic assays. Theenzymatic assays can be any enzymatic assays known to those of skill inthe art to be useful for detecting, measuring or monitoring one or moreof the biomarkers of the invention.

In certain embodiments, the enzymatic assays can be according topublished application JP 2002-17938 (Kishimoto et al., 2002, Method ofMeasuring Phospholipid), or according to Kishimoto et al., 2002,Clinical Biochem. 35:411-416, the contents of which are herebyincorporated by reference in their entireties.

In certain embodiments, the amount of lysophosphatidylcholine can bemeasured by contacting a sample of the invention with an enzyme capableof hydrolyzing lysophosphatidylcholine to yieldglycerophosphorylcholine. The enzyme can be any such enzyme known tothose of skill in the art. Exemplary enzymes include lysophospholipasessuch as EC 3.1.1.5 (commercially available from, e.g., Asahi ChemicalCo.). In certain embodiments, the lysophospholipase preferentiallyhydrolyzes lysophospholipids relative to other phospholipids. In certainembodiments, the lysophospholipase is from Bacillus. In certainembodiments, the lysophospholipase is according to JP 2002-17938.

The resulting glycerophosphorylcholine can be detected, measured ormonitored according to any technique apparent to those of skill in theart. For instance, in certain embodiments, the glycerophosphorylcholinecan be contacted with a glycerophosphorylcholine diesterase known tothose of skill in the art (e.g. EC 3.1.4.2) under conditions suitable toyield choline. The resulting choline can be contacted with a cholineoxidase known to those of skill in the art (e.g. EC 1.1.3.17) underconditions suitable to yield peroxide. Use of the choline oxidaseenables the method to detect lysophosphatidylcholine in preference toother lysophospholipids such as lysophospholipids comprising serine orethanolamine. The resulting peroxide can be detected by any techniqueapparent to those of skill in the art including, for example,colorimetric techniques.

The detection of hydrogen peroxide can be accomplished by any techniqueapparent to one of skill in the art. Exemplary techniques includechemiluminescence (Kiba et al., 2003, Analytical Science 19(6):823-827),fluorescence (Zhang et al., 199, Talanta 48(5):1031-1038; Chen et al.,2001, Analytica Chimica 434(1):51-58), and spectrophotometry (Pappas etal., 2002, Analytica Chimica 455(2):305-313). Other exemplary techniquesinclude metal complexes (Paleologos, 2002, Analytical Chemistry74(1):100-106) as well as redox mediated electrochemical detection(e.g., commercially available glucose meters).

In advantageous embodiments, peroxidase activity can be detected with afluorogenic substrate. Such embodiments provide rapid and sensitivetechniques for the detection of the amount of lysophosphatidylcholine inthe sample. These techniques thus provide rapid and sensitive assays forthe advanced detection of a systemic inflammatory condition as describedherein. The fluorogenic substrate can be any fluorogenic substrate knownto those of skill in the art to be capable of conversion to afluorescent product by a peroxidase in the presence of peroxide undersuitable conditions, e.g. with water and oxygen. In particularembodiments, the fluorogenic substrate is10-acetyl-3,7-dihydroxyphenoxazine. This substrate can be obtained fromcommercial suppliers (e.g. Amplex Red, Invitrogen). Those of skill inthe art will recognize that this fluorogenic substrate can be convertedto the fluorescent product 7-hydroxy-3H-phenoxazin-3-one (resorufin),detectable by techniques apparent to those of skill in the art. Usefuldetection techniques include, of course, fluorescence detection.Preferably, the detection methods are carried out under conditions inwhich the product can be formed and detected. Useful conditions andresults are described in the working examples below.

In certain embodiments, the glycerophosphorylcholine can be contactedwith a glycerophosphorylcholine phosphodiesterase known to those ofskill in the art under conditions suitable to yieldglycerol-3-phosphate. The resulting glycerol-3-phosphate can becontacted with a glycerol-3-phosphate oxidase known to those of skill inthe art under conditions suitable to yield peroxide. Usefulglycerol-3-phosphate oxidases include those derived from Streptococcus,Aerococcus, and Pediococcus, and those described in JP 2002-17938. Theresulting peroxide can be detected by any technique apparent to those ofskill in the art including, for example, colorimetric techniques.

In certain embodiments, the glycerophosphorylcholine can be contactedwith a glycerophosphorylcholine phosphodiesterase known to those ofskill in the art under conditions suitable to yieldglycerol-3-phosphate. The resulting glycerol-3-phosphate can becontacted with a glycerol-3-phosphate dehydrogenase known to those ofskill in the art under conditions suitable to yield a detectableproduct. For instance, the contacting can be in the presence of NAD⁺ toyield detectable NADH. The contacting can also be in the presence ofNADP⁺ to yield detectable NADPH.

Techniques for detecting, measuring or monitoring detectable productssuch as peroxide, NADH and NADPH are well known to those of skill in theart. Useful techniques are described in JP 2002-17938, Misaki, 1999,Modern Medical Laboratory 27(8): 973-980, (1999), Japanese Patent No.1594750, Japanese Patent Laid-Open No. 05-229993, and Aoyama, 1997,Journal of Medical Technology 14: 1014-1019, the contents of which arehereby incorporated by reference in their entireties.

6.7 Measurement of Biomarkers

The biological sample can be processed or purified according to thejudgment of those of skill in the art based on, for example, the type ofbiomarker and the measurement technique. For instance, when thebiomarker is a lipid or phospholipid metabolite, the sample can beprocessed by extraction and/or chromatography. When the biomarker is aprotein or peptide, for example when a panel of biomarkers is to beevaluated, the sample can be processed by precipitation, centrifugation,filtration and/or chromatography. When the biomarker is a nucleic acid,for example when a panel of biomarkers is to be evaluated, the samplecan be processed to isolate nucleic acids by extraction, precipitationand/or chromatography.

These amounts can be determined through the use of any reproduciblemeasurement technique or combination of measurement techniques. Suchtechniques include those that are well known in the art including anytechnique described herein. Typically, such techniques are used tomeasure amounts using a biological sample taken from a subject at asingle point in time or multiple samples taken at multiple points intime.

In certain embodiments, methods of detection of the biomarker involvetheir detection via interaction with a biomarker-specific antibody, forexample, antibodies directed to the biomarker of the invention.Antibodies can be generated utilizing standard techniques well known tothose of skill in the art. In specific embodiments, antibodies can bepolyclonal, or more preferably, monoclonal. An intact antibody, or anantibody fragment (e.g., scFv, Fab or F(ab′)₂) can, for example, beused.

For example, antibodies, or fragments of antibodies, specific for abiomarker can be used to quantitatively or qualitatively detect thepresence of a biomarker. This can be accomplished, for example, byimmunofluorescence techniques. Antibodies (or fragments thereof) can,additionally, be employed histologically, as in immunofluorescence orimmunoelectron microscopy, for in situ detection of a biomarker. In situdetection can be accomplished by removing a biological sample (e.g., abiopsy specimen) from a subject, and applying thereto a labeled antibodythat is directed to a biomarker. The antibody (or fragment) ispreferably applied by overlaying the antibody (or fragment) onto abiological sample. Through the use of such a procedure, it is possibleto determine not only the presence of the biomarker, but also itsdistribution, in a particular sample. A wide variety of well-knownhistological methods (such as staining procedures) can be utilized toachieve such in situ detection.

Immunoassays for a biomarker typically comprise incubating a biologicalsample of a detectably labeled antibody capable of identifying abiomarker, and detecting the bound antibody by any of a number oftechniques well-known in the art. As discussed in more detail, below,the term “labeled” can refer to direct labeling of the antibody via,e.g., coupling (i.e., physically linking) a detectable substance to theantibody, and can also refer to indirect labeling of the antibody byreactivity with another reagent that is directly labeled. Examples ofindirect labeling include detection of a primary antibody using afluorescently labeled secondary antibody.

The biological sample can be brought in contact with and immobilizedonto a solid phase support or carrier such as nitrocellulose, or othersolid support which is capable of immobilizing cells, cell particles orsoluble proteins. The support can then be washed with suitable buffersfollowed by treatment with the detectably labeled fingerprintgene-specific antibody. The solid phase support can then be washed withthe buffer a second time to remove unbound antibody. The amount of boundlabel on solid support can then be detected by conventional methods.

By “solid phase support or carrier” is intended any support capable ofbinding an antigen or an antibody. Well-known supports or carriersinclude glass, polystyrene, polypropylene, polyethylene, dextran, nylon,amylases, natural and modified celluloses, polyacrylamides andmagnetite. The nature of the carrier can be either soluble to someextent or insoluble for the purposes of the present invention. Thesupport material can have virtually any possible structuralconfiguration so long as the coupled molecule is capable of binding toan antigen or antibody. Thus, the support configuration can bespherical, as in a bead, or cylindrical, as in the inside surface of atest tube, or the external surface of a rod. Alternatively, the surfacecan be flat such as a sheet, test strip, etc. Preferred supports includepolystyrene beads. Those skilled in the art will know many othersuitable carriers for binding antibody or antigen, or will be able toascertain the same by use of routine experimentation.

One of the ways in which an antibody specific for a biomarker can bedetectably labeled is by linking the same to an enzyme and use in anenzyme immunoassay (EIA) (Voller, 1978, “The Enzyme Linked ImmunosorbentAssay (ELISA)”, Diagnostic Horizons 2:1-7, Microbiological AssociatesQuarterly Publication, Walkersville, Md.; Voller et al., 1978, J. Clin.Pathol. 31:507-520; Butler, J. E., 1981, Meth. Enzymol. 73:482-523;Maggio, E. (ed.), 1980, Enzyme Immunoassay, CRC Press, Boca Raton, Fla.;Ishikawa, E. et al., (eds.), 1981, Enzyme Immunoassay, Kgaku Shoin,Tokyo, each of which is hereby incorporated by reference in itsentirety). The enzyme which is bound to the antibody will react with anappropriate substrate, preferably a chromogenic substrate, in such amanner as to produce a chemical moiety which can be detected, forexample, by spectrophotometric, fluorimetric or by visual means. Enzymeswhich can be used to detectably label the antibody include, but are notlimited to, malate dehydrogenase, staphylococcal nuclease,delta-5-steroid isomerase, yeast alcohol dehydrogenase,alpha-glycerophosphate, dehydrogenase, triose phosphate isomerase,horseradish peroxidase, alkaline phosphatase, asparaginase, glucoseoxidase, beta-galactosidase, ribonuclease, urease, catalase,glucose-6-phosphate dehydrogenase, glucoamylase andacetylcholinesterase. The detection can be accomplished by colorimetricmethods which employ a chromogenic substrate for the enzyme. Detectioncan also be accomplished by visual comparison of the extent of enzymaticreaction of a substrate in comparison with similarly prepared standards.

Detection can also be accomplished using any of a variety of otherimmunoassays. For example, by radioactively labeling the antibodies orantibody fragments, it is possible to detect a biomarker through the useof a radioimmunoassay (RIA) (see, for example, Weintraub, B., Principlesof Radioimmunoassays, Seventh Training Course on Radioligand AssayTechniques, The Endocrine Society, March, 1986, which is incorporated byreference herein). The radioactive isotope (e.g., ¹²⁵I, ¹³¹I, ³⁵S or ³H)can be detected by such means as the use of a gamma counter or ascintillation counter or by autoradiography.

It is also possible to label the antibody with a fluorescent compound.When the fluorescently labeled antibody is exposed to light of theproper wavelength, its presence can then be detected due tofluorescence. Among the most commonly used fluorescent labelingcompounds are fluorescein isothiocyanate, rhodamine, phycoerythrin,phycocyanin, allophycocyanin, o-phthaldehyde and fluorescamine.

The antibody can also be detectably labeled using fluorescence emittingmetals such as ¹⁵²Eu or others of the lanthanide series. These metalscan be attached to the antibody using such metal chelating groups asdiethylenetriaminepentacetic acid (DTPA) or ethylenediaminetetraaceticacid (EDTA).

The antibody also can be detectably labeled by coupling it to achemiluminescent compound. The presence of the chemiluminescent-taggedantibody is then determined by detecting the presence of luminescencethat arises during the course of a chemical reaction. Examples ofparticularly useful chemiluminescent labeling compounds are luminol,isoluminol, theromatic acridinium ester, imidazole, acridinium salt andoxalate ester.

Likewise, a bioluminescent compound can be used to label the antibody ofthe present invention. Bioluminescence is a type of chemiluminescencefound in biological systems in, which a catalytic protein increases theefficiency of the chemiluminescent reaction. The presence of abioluminescent protein is determined by detecting the presence ofluminescence. Important bioluminescent compounds for purposes oflabeling are luciferin, luciferase and aequorin.

In another embodiment, specific binding molecules other than antibodies,such as aptamers, may be used to bind the biomarkers.

Amounts of biomarkers may also be determined by the use of one or moreof the following methods described below. For example, methods mayinclude nuclear magnetic resonance (NMR) spectroscopy, a massspectrometry method, such as electrospray ionization mass spectrometry(ESI-MS), ESI-MS/MS, ESI-MS/(MS)^(n) (n is an integer greater thanzero), matrix-assisted laser desorption ionization time-of-flight massspectrometry (MALDI-TOF-MS), surface-enhanced laserdesorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS),desorption/ionization on silicon (DIOS), secondary ion mass spectrometry(SIMS), quadrupole time-of-flight (Q-TOF), atmospheric pressure chemicalionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI-(MS)^(n),atmospheric pressure photoionization mass spectrometry (APPI-MS),APPI-MS/MS, and APPI-(MS)^(n). Other mass spectrometry methods mayinclude, inter alia, quadrupole, Fourier transform mass spectrometry(FTMS) and ion trap. Other suitable methods may include chemicalextraction partitioning, column chromatography, ion exchangechromatography, hydrophobic (reverse phase) liquid chromatography,isoelectric focusing, one-dimensional polyacrylamide gel electrophoresis(PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) orother chromatography, such as thin-layer, gas or liquid chromatography,or any combination thereof. In one embodiment, the biological sample maybe fractionated prior to application of the separation method.

In specific embodiments of the invention, the biomarkers are nucleicacids. Such biomarkers and corresponding amounts may be generated, forexample, by detecting the expression product (e.g., a polynucleotide orpolypeptide) of one or more genes known to those of skill in the art. Ina specific embodiment, the biomarkers and corresponding amounts in abiomarker profile are obtained by detecting and/or analyzing one or morenucleic acids using any method well known to those skilled in the artincluding, but by no means limited to, hybridization, microarrayanalysis, RT-PCR, nuclease protection assays and Northern blot analysis.As will be recognized by those of skill in the art, in convenientembodiments, the biological sample can be split, with one portionevaluated for nucleic acid biomarkers, and another portion evaluated forother biomarkers such as proteins, peptides, lipids, phospholipids andmetabolites. In fact, the biological sample can be divided as many timesas desired by the practitioner of skill to facilitate evaluation ormeasurement of each biomarker in a plurality or panel of biomarkers.

In certain embodiments, the amounts for biomarkers in a biomarkerprofile are obtained by hybridizing to detectably labeled nucleic acidsrepresenting or corresponding to the nucleic acid sequences in mRNAtranscripts present in a biological sample (e.g., fluorescently labeledcDNA synthesized from the sample) to a microarray comprising one or moreprobe spots.

Several chromatographic techniques may be used to separate biomarkers.For example, amplification products may be separated by agarose,agarose-acrylamide or polyacrylamide gel electrophoresis usingconventional methods. See Sambrook et al., 2001. Several techniques fordetecting biomarkers quantitatively without electrophoresis may also beused according to the invention (see, e.g., PCR Protocols, A Guide toMethods and Applications, Innis et al., 1990, Academic Press, Inc. N.Y.,which is hereby incorporated by reference). For example, chromatographictechniques may be employed to effect separation. There are many kinds ofchromatography which may be used in the present invention: adsorption,partition, ion-exchange and molecular sieve, HPLC, and many specializedtechniques for using them including column, paper, thin-layer and gaschromatography (Freifelder, Physical Biochemistry Applications toBiochemistry and Molecular Biology, 2nd ed., Wm. Freeman and Co., NewYork, N.Y., 1982, which is hereby incorporated by reference).

In certain embodiments, one or more of the biomarkers is a protein.Standard techniques may be utilized for determining the amount of theprotein or proteins of interest present in a sample. For example,standard techniques can be employed using, e.g., immunoassays such as,for example Western blot, immunoprecipitation followed by sodium dodecylsulfate polyacrylamide gel electrophoresis, (SDS-PAGE),immunocytochemistry, and the like to determine the amount of protein orproteins of interest present in a sample. One exemplary agent fordetecting a protein of interest is an antibody capable of specificallybinding to a protein of interest, preferably an antibody detectablylabeled, either directly or indirectly.

For such detection methods, if desired a protein from the sample to beanalyzed can easily be isolated using techniques which are well known tothose of skill in the art. Protein isolation methods can, for example,be such as those described in Harlow and Lane, 1988, Antibodies: ALaboratory Manual. Cold Spring Harbor Laboratory Press (Cold SpringHarbor, N.Y.), which is incorporated by reference herein in itsentirety.

In certain embodiments, methods of detection of the protein or proteinsof interest involve their detection via interaction with aprotein-specific antibody. For example, antibodies directed to a proteinof interest. Antibodies can be generated utilizing standard techniqueswell known to those of skill in the art. In specific embodiments,antibodies can be polyclonal, or more preferably, monoclonal. An intactantibody, or an antibody fragment (e.g., scFv, Fab or F(ab′)₂) can, forexample, be used. Exemplary immunoassays are described above.

6.8 Advanced Detection of Sepsis

In certain methods of the invention, the amount oflysophosphatidylcholine in the subject is used for the advanceddetection of sepsis, for example, along with one or more clinicalmarkers and/or one or more biomarkers, as described above. As describedabove, the amount of lysophosphatidylcholine can be measured directly,or a measurement can be made that correlates to that amount. In certainembodiments, free lysophosphatidylcholine is measured. In certainembodiments, bound lysophosphatidylcholine is measured. In certainembodiments, total lysophosphatidylcholine is measured.

In certain methods of the invention, the amount of one or more compoundsof formula (I) in the subject are used for the advanced detection ofsepsis.

In certain methods of the invention, the amount of totallysophosphatidylcholine and one or more one or more compounds of formula(I) in the subject are used for the advanced detection of sepsis.

In some embodiments, a single sample from the subject is sufficient forthe advanced detection of sepsis. In such embodiments, the amount oflysophosphatidylcholine can be compared to an internal reference in thebiological sample that is present at a relatively constant amount inindividuals similar to the subject. The internal reference can be anyreference judged suitable to one of skill in the art and is preferablynot related to the biomarker or to systemic inflammatory conditions. Incertain embodiments, the internal reference is lysophosphatidylcholine,lysophosphatidylethanolamine or lysophosphatidylserine.

In some embodiments, a plurality of biological samples from the subjectare evaluated for the advanced detection of sepsis. In such embodiments,change in the amount of lysophosphatidylcholine detects sepsis orindicates increased likelihood of onset of sepsis.

In certain embodiments, decreasing amounts of lysophosphatidylcholinedetect sepsis or indicate increased likelihood of onset of sepsis. Forinstance, in certain embodiments, a second amount that is less than 95%,90%, 80%, 75%, 50%, 33%, 25%, 20% or 10% of a previous amount detectssepsis or indicates increased likelihood of onset of sepsis

In certain embodiments, advanced detection of sepsis can be based on acomparison of the amount of lysophosphatidylcholine in a sample of thesubject to a reference amount of lysophosphatidylcholine. Referenceamounts are described in the section below. Significantly, the amount ofthe reference amount need not be obtained or measured by a practitionerof a method of the invention. Instead, the reference amount can beidentified by consultation of amounts of the reference in referencepopulations available to those of skill in the art. Such amounts can bepublished, for example, in scientific literature on electronicdatabases.

In preferred embodiments, the reference amount is measured by the sametechnique or a comparable technique used to measure the amount in thesample. For instance, preferably, if free lysophosphatidylcholine ismeasured in the sample, the reference amount can be based on freelysophosphatidylcholine in a reference subject or a referencepopulation. For instance, preferably, if bound lysophosphatidylcholineis measured in the sample, the reference amount can be based on boundlysophosphatidylcholine. Of course, if total lysophosphatidylcholine andthe reference amount are measured by different techniques, correlationof the two amounts should be within the ability of the practitioner ofskill.

If lysophosphatidylcholine is measured in the sample, the referenceamount can be based on the lysophosphatidylcholine in a referencesubject or reference population. Of course, if lysophosphatidylcholineand the reference amount are measured by different techniques,correlation of the two amounts should be within the ability of thepractitioner of skill

When a reference amount is used, the difference between the referenceamount and the amount in the test subject is used by a practitioner ofskill in the art to detect sepsis. In certain embodiments, if the amountin the test subject is between 10-190%, 20-180%, 30-170%, 40-160%,50-150%, 75-125%, 80-120%, 90-110% or 95-105% of the reference amount,sepsis or an increased likelihood of onset of sepsis is indicated.

If a threshold reference amount is used, the difference between thethreshold and the amount in the test subject is used by a practitionerof skill in the art to detect sepsis or to indicate an increasedlikelihood of onset of sepsis. In certain embodiments, if the amount inthe test subject is below, or substantially below, the thresholdreference amount, sepsis is detected, and if the amount in the testsubject is above, or substantially above, the threshold referenceamount, s sepsis is not detected.

In certain embodiments, the difference between the amount oflysophosphatidylcholine in the test subject and the reference amountcorrelates inversely with detection of sepsis or with an increasedlikelihood of onset of sepsis. Such correlation can be determined bythose of skill in the art.

When reference amounts from a plurality of reference subjects are used,the evaluation can be based on any statistical technique known to thoseof skill in the art. Similarly, when a plurality of biomarkers are used,the advanced detection can be based on the plurality of amountsaccording to techniques known to those of skill in the art, such asthose described in U.S. Patent Application Publication Nos. 20030194752,20040096917, 20040097460, 20040106142, 20040157242, and U.S. ProvisionalApplication Nos. 60/671,620, filed Apr. 15, 2005, 60/671,941, filed Apr.15, 2005, and 60/674,046, filed Apr. 22, 2005, the contents of which arehereby incorporated by reference in their entireties.

6.9 Reference Amount of Lysophosphatidylcholine

In certain methods of the invention, the amount oflysophosphatidylcholine of the subject is compared to a correspondingreference amount of lysophosphatidylcholine. The reference amount istypically the amount of lysophosphatidylcholine in a reference subject(not the subject of the method) that has, or will have within a definedperiod of time, a known systemic inflammatory condition. While notintending to be bound by any particular theory of operation, the presentinvention is based, in part, on the discovery of a correlation betweentotal amount lysophosphatidylcholine and a systemic inflammatorycondition in a subject. Accordingly, one practicing a method of theinvention can compare the amount of lysophosphatidylcholine in a subjectto a reference amount of lysophosphatidylcholine in order to make aprognosis or diagnosis of the systemic inflammatory condition.

In certain methods of the invention, the amount of thelysophosphatidylcholine of the subject is compared to a correspondingreference amount of the lysophosphatidylcholine. The reference amount istypically the amount of the same lysophosphatidylcholine, or aderivative thereof, in a reference subject (not the subject of themethod) that has, or will have within a defined period of time, a knownsystemic inflammatory condition. For example, the reference amount for1-O-palmitoyl-2-lyso-sn-glycero-3-phosphocholine should be the amount of1-O-palmitoyl-2-lyso-sn-glycero-3-phosphocholine, or a derivativethereof, in the reference subject. While not intending to be bound byany particular theory of operation, the present invention is based, inpart, on the discovery of a correlation between alysophosphatidylcholine of the invention and advanced detection ofsepsis in a subject. Accordingly, one practicing a method of theinvention can compare the amount of a lysophosphatidylcholine in asubject to a reference amount of that lysophosphatidylcholine in orderto detect sepsis.

Advantageously, in order to practice methods of the invention, one neednot gather reference amounts of lysophosphatidylcholine in referencepopulations. Such reference amounts can be identified in sourcesavailable to those of skill in the art, such as public or privatedatabases, or by reference to the data provided herein. As such, in themethods that use a reference amount of lysophosphatidylcholine, one needonly make the comparison described in the method.

A reference amount can be measured according to techniques known tothose of skill in the art including those described herein.Advantageously, in certain embodiments, the amount oflysophosphatidylcholine in the reference subject and the amount oflysophosphatidylcholine in the test subject are obtained by the sametechnique.

The reference subject can be any subject that presents, or that willpresent within a defined period of time, symptoms of the systemicinflammatory condition according to one of skill in the art. In certainembodiments, the reference amount is obtained at a time when thereference subject is presenting the symptoms. In certain embodiments,the reference amount can be obtained at a time before or a time afterthe reference subject presents symptoms of, or is diagnosed with, thesystemic inflammatory condition. For instance, in certain embodiments,reference amounts are obtained from reference subjects 48, 36, 24 or 12hours prior to onset of sepsis. Those of skill in the art will recognizethat such amounts can be obtained by measuring amounts in a referencepopulation diagnosed with sepsis and following the diagnoses of thereference subject at a plurality of time points.

The reference subject can have any systemic inflammatory condition orcan be free of a systemic inflammatory condition. In certainembodiments, the reference subject can be SIRS-negative or presentsymptoms of SIRS, sepsis, severe sepsis, septic shock, multiple organdysfunction or mortality. Such reference amounts can be used for theadvanced detection of the condition.

Methods for the diagnosis of the systemic inflammatory condition are tobe carried out according to the knowledge of those of skill in the art.Such methods are routine and will not be described herein.

In certain embodiments, the advanced detection of the systemicinflammatory condition is based on a threshold reference amount. Athreshold reference amount is an absolute value for the amount thatdetects the systemic inflammatory condition. For instance, a thresholdreference amount of 100 for a biomarker of the invention can detectsepsis when the test subject has an amount of the biomarker that is lessthan 100 (or greater than 100 in alternative embodiments). Thresholdreference amounts can be determined using statistical techniques knownto those of skill in the art based on reference amounts obtained fromreference subjects. For instance, a threshold for a particular systemicinflammatory condition can be determined so that a new reference subjectcan have a advanced detection within a confidence interval suitable tothose of skill in the art, for instance with 60%, 70%, 80%, 85%, 90%,95% or 99% confidence.

6.10 Monitors for the Advanced Detection of Sepsis

The invention also provides monitors that are useful for the advanceddetection of sepsis in a subject. In certain embodiments the monitorcomprises a sensor module capable of measuring one or more clinicalmarkers of the subject and a chemistry module capable of measuring anamount of lysophosphatidylcholine in fluid or tissue of the subject.

In particular embodiments, the sensor module is capable of measuring twoor more clinical markers. In preferred embodiments, the sensor module iscapable of measuring the subject's temperature and the subject'srespiratory rate. In certain embodiments the monitor comprises a firstsensor module capable of measuring the subject's temperature, a secondsensor module capable of measuring the subject's respiratory rate, and achemistry module capable of measuring an amount oflysophosphatidylcholine in fluid or tissue of the subject. In particularembodiments, the first sensor module and the second sensor module arecapable of being combined into one sensor module.

In certain embodiments the monitor comprises a sensor module thatmeasures one or more clinical markers of the subject and a chemistrymodule that measures an amount of lysophosphatidylcholine in fluid ortissue of the subject.

In particular embodiments, the sensor module measures two or moreclinical markers. In preferred embodiments, the sensor module measuresthe subject's temperature and the subject's respiratory rate. In certainembodiments the monitor comprises a first sensor module that measuresthe subject's temperature, a second sensor module that measures thesubject's respiratory rate, and a chemistry module that measures anamount of lysophosphatidylcholine in fluid or tissue of the subject. Inparticular embodiments, the first sensor module and the second sensormodule are combined into one sensor module.

In certain embodiments, the monitor is capable of measuring at a singletime point. In certain embodiments, the monitor is capable ofmeasurement at a plurality of time points. In certain embodiments, themonitor is capable of measurement at a plurality of time points ondemand by the user. In certain embodiments, the monitor is capable ofmeasurement at a plurality of time points at user selected times (e.g.,hourly, every eight hours, every twelve hours, daily, etc.). In certainembodiments, the monitor is capable of measurement at a plurality oftime points automatically at regular intervals (e.g., every 5 minutes,every 10 minutes, etc.). In certain embodiments, the monitor is capableof measurement at a plurality of time points automatically andcontinuously.

In certain embodiments, the monitor measures at a single time point. Incertain embodiments, the monitor measures at a plurality of time points.In certain embodiments, the monitor measures at a plurality of timepoints on demand by the user. In certain embodiments, the monitormeasures at a plurality of time points at user selected times (e.g.,hourly, every eight hours, every twelve hours, daily, etc.). In certainembodiments, the monitor measures at a plurality of time pointsautomatically at regular intervals (e.g., every 5 minutes, every 10minutes, etc.). In certain embodiments, the monitor measures at aplurality of time points automatically and continuously.

In a preferred embodiment, the monitor is capable of continuousmeasuring.

In certain embodiments, the individual sensor modules and chemistrymodules of the monitor operate separately of one another.

In certain embodiments, the invention provides for a stand-alone monitorcapable of measuring at a plurality of time points an amount oflysophosphatidylcholine in fluid or tissues of the subject.

In certain embodiments, the invention provides for a stand-alone monitorthat measures lysophosphatidylcholine in fluid or tissues of thesubject. In certain embodiments, the invention provides for astand-alone monitor that measures a plurality of time points an amountof lysophosphatidylcholine in fluid or tissues of the subject.

In certain embodiments, the first sensor module is capable of connectingto a temperature probe in the subject. In particular embodiments, thetemperature probe is capable of being applied externally to thesubject's skin, for example, it is capable of being secured in placeunder the subject's arm pit. In particular embodiments, the temperatureprobe is capable of being applied internally to the subject, forexample, it is capable of being applied orally or rectally to thesubject. In preferred embodiments, the internal temperature probe iscapable of being applied to the subject's esophagus or rectum.

In certain embodiments, the first sensor module is connected to atemperature probe in the subject. In particular embodiments, thetemperature probe is applied externally to the subject's skin, forexample, it is secured in place under the subject's arm pit. Inparticular embodiments, the temperature probe is applied internally tothe subject, for example, it is applied orally or rectally to thesubject. In preferred embodiments, the internal temperature probe isapplied to the subject's esophagus or rectum.

In certain embodiments, the second sensor module is capable of beingconnected to any device suitable for the measurement of respiratory ratein a subject, including a pressure sensor, a humidity sensor, athermistor or a motion sensor. In certain embodiments, the second sensormodule is capable of being connected to a motion sensor. In particularembodiments, the motion sensor is a flow meter. In particularembodiments, the flow meter is in a respirator.

In certain embodiments, the second sensor module is connected to anydevice suitable for the measurement of respiratory rate in a subject,including a pressure sensor, a humidity sensor, a thermistor or a motionsensor. In certain embodiments, the second sensor module is connected toa motion sensor. In particular embodiments, the motion sensor is a flowmeter. In particular embodiments, the flow meter is in a respirator.

In certain embodiments, the chemistry module is capable of contacting asample from the fluid or tissue of the subject with: an enzyme orreagent capable of reacting lysophosphatidylcholine to formglycerophosphatidylcholine, an enzyme or reagent capable of reactingglycerophosphatidylcholine to form choline, an enzyme or reagent capableof reacting choline, water and oxygen to form peroxide, a peroxidase anda fluorogenic substrate of said peroxidase, under conditions suitablefor formation of a fluorescent product wherein the fluorescent productindicates lysophosphatidylcholine.

In certain embodiments, the sample from the fluid or tissue of thesubject in the chemistry module is contacted with: an enzyme or reagentcapable of reacting lysophosphatidylcholine to formglycerophosphatidylcholine, an enzyme or reagent capable of reactingglycerophosphatidylcholine to form choline, an enzyme or reagent capableof reacting choline, water and oxygen to form peroxide, a peroxidase anda fluorogenic substrate of said peroxidase, under conditions suitablefor formation of a fluorescent product wherein the fluorescent productindicates lysophosphatidylcholine.

The monitors of the present invention may contain, as part of thechemistry module, reagents useful for the measurement oflysophosphatidylcholine in the fluid or tissue of the subject. Incertain embodiments, the reagents comprise one or more enzymes and oneor more substrates useful for detection of lysophosphatidylcholine. Inparticular embodiments, the chemistry module can comprise a fluorogenicsubstrate useful for the measurement of lysophosphatidylcholine. Certainchemistry modules comprise an enzyme or reagent capable of reactinglysophosphatidylcholine to form glycerophosphatidylcholine undersuitable conditions, an enzyme or reagent capable of reactingglycerophosphatidylcholine to form choline under suitable conditions, anenzyme or reagent capable of reacting choline to form peroxide undersuitable conditions, a peroxidase and a fluorogenic substrate of saidperoxidase. Certain chemistry modules comprise a lysophospholipase, aglycerophosphatidylcholine diesterase, a choline oxidase, a peroxidaseand 10-acetyl-3,7-dihydroxyphenoxazine. Certain chemistry modulescomprise EC 3.1.1.5, EC 3.1.4.2, EC 1.1.3.17, horseradish peroxidase and10-acetyl-3,7-dihydroxyphenoxazine. The chemistry modules can furthercomprise one or more reference standards for evaluating the totallysophosphatidylcholine according to methods of the invention.

In particular embodiments, the reagents useful for the measurement oflysophosphatidylcholine in the fluid or tissue of the subject are on atest strip in the chemistry module, onto which the sample of fluid ortissue is applied.

In certain embodiments, the monitors further comprise a label orlabeling with instructions for carrying out a method of the invention.For example, the label or labeling can provide a reference amount orreference amounts of lysophosphatidylcholine corresponding to one ormore systemic inflammatory conditions, such as SIRS, sepsis, severesepsis, septic shock or multiple organ dysfunction. The label orlabeling can provide one or more threshold reference amounts oflysophosphatidylcholine corresponding to one or more of systemicinflammatory condition, or to conversion of one systemic inflammatorycondition into another, for example, for conversion of SIRS-positiveinto sepsis. Further, the label or labeling can provide citations orlinks to sources of such reference amounts. The label or labeling canalso provide reference values for clinical markers, such as temperatureand respiratory rate, corresponding to one or more systemic inflammatoryconditions. The label or labeling can provide one or more thresholdreference values for clinical markers, such as temperature andrespiratory rate, corresponding to one or more systemic inflammatorycondition, or to conversion of one systemic inflammatory condition intoanother, for example, for conversion of SIRS-positive into sepsis.Further, the label or labeling can provide citations or links to sourcesof such reference amounts.

6.11 Systems for the Advanced Detection of Sepsis

The invention also provides systems that are useful for the advanceddetection of sepsis in a subject. In certain embodiments the systemcomprises: (a) one or more monitors of the invention, (b) acomputational device capable of combining the measurements obtained fromthe one of more monitors into a result, and (c) a module capable ofstoring, displaying and/or transmitting the result. In such systems, thecomputational device comprises: (i) a device capable of receiving themeasurements from the one or more monitors; (ii) a microprocessor withan algorithm capable of combining the measurements into a result; and(iii) a device capable of transmitting the result to the module capableof storing, displaying and/or transmitting.

In certain embodiments the system comprises: (a) one or more monitors ofthe invention, (b) a computational device that combines the measurementsobtained from the one of more monitors into a result, and (c) a modulecapable of storing, displaying and/or transmitting the result. In suchsystems, the computational device comprises: (i) a device that receivesthe measurements from the one or more monitors; (ii) a microprocessorwith an algorithm that combines the measurements into a result; and(iii) a device that transmits the result to the module capable ofstoring, displaying and/or transmitting.

In certain embodiments, the system comprises a single sensor modulecapable of measuring one or more clinical markers of the subject. Insuch systems, the computational device is capable of combining into aresult the clinical marker values from the sensor module and anylysophosphatidylcholine and/or biomarker values obtained independentlyfrom a laboratory to which the fluid or tissue samples of the subjecthave been sent for analysis. FIG. 1 illustrates this embodiment of thesystem.

In certain embodiments, the system comprises a single sensor module thatmeasures one or more clinical markers of the subject. In such systems,the computational device combines into a result the clinical markervalues from the sensor module and any lysophosphatidylcholine and/orbiomarker values obtained independently from a laboratory to which thefluid or tissue samples of the subject have been sent for analysis. FIG.1 illustrates this embodiment of the system.

In certain embodiments, the system comprises a sensor module capable ofmeasuring one or more clinical markers of the subject and a chemistrymodule capable of measuring an amount of lysophosphatidylcholine influid or tissue of the subject. In such embodiments, the computationaldevice is capable of combining the clinical marker andlysophosphatidylcholine measurements from the modules into a result.

In certain embodiments, the system comprises a sensor module thatmeasures one or more clinical markers of the subject and a chemistrymodule that measures an amount of lysophosphatidylcholine in fluid ortissue of the subject. In such embodiments, the computational devicecombines the clinical marker and lysophosphatidylcholine measurementsfrom the modules into a result.

In certain embodiments, the system comprises a first sensor modulecapable of measuring the temperature of the subject, a second sensormodule capable of measuring the respiratory rate of the subject, and achemistry module capable of measuring an amount oflysophosphatidylcholine in fluid or tissue of the subject. In suchembodiments, the computational device is capable of combining thetemperature, respiratory rate, and lysophosphatidylcholine measurementsfrom the modules into a result.

In certain embodiments, the system comprises a first sensor module thatmeasures the temperature of the subject, a second sensor module thatmeasures the respiratory rate of the subject, and a chemistry modulethat measures an amount of lysophosphatidylcholine in fluid or tissue ofthe subject. In such embodiments, the computational device combines thetemperature, respiratory rate, and lysophosphatidylcholine measurementsfrom the modules into a result.

In certain embodiments, the system additionally comprises a secondchemistry module capable of measuring a second biomarker in fluid ortissue of the subject. In such an embodiment, the computational deviceis capable of combining the temperature, respiratory rate,lysophosphatidylcholine, and second biomarker values into a result.

In certain embodiments, the system additionally comprises a secondchemistry module that measures a second biomarker in fluid or tissue ofthe subject. In such an embodiment, the computational device combinesthe temperature, respiratory rate, lysophosphatidylcholine, and secondbiomarker values into a result.

In certain aspects, the invention provides systems for the advanceddetection of sepsis in a subject comprising: (a) a sensor module capableof measuring at a plurality of time points one or more clinical markersof the subject, (b) a chemistry module capable of measuring at aplurality of time points an amount of lysophosphatidylcholine orprocalcitonin in fluid or tissue of the subject, (c) a computationaldevice capable of combining the measurements in (a) and the measurementsin (b) into a result, comprising: (i) a device capable of receiving themeasurements in (a) and the measurements in (b) from the one or moremodules; (ii) a microprocessor with an algorithm capable of combiningthe measurements into a result; and (iii) a device capable oftransmitting the result to a module capable of storing, displayingand/or transmitting; and (d) a module capable of storing, displaying ortransmitting the result.

In certain aspects, the invention provides systems for the advanceddetection of sepsis in a subject comprising: (a) a sensor module thatmeasures at a plurality of time points one or more clinical markers ofthe subject, (b) a chemistry module that measures at a plurality of timepoints an amount of lysophosphatidylcholine or procalcitonin in fluid ortissue of the subject, (c) a computational device that combines themeasurements in (a) and the measurements in (b) into a result,comprising: (i) a device that receives the measurements in (a) and themeasurements in (b) from the one or more modules; (ii) a microprocessorwith an algorithm that combines the measurements into a result; and(iii) a device that transmits the result to a module that stores,displays and/or transmits; and (d) a module that stores, displays ortransmits the result.

In certain embodiments, the monitors are capable of measuring at asingle time point. In certain embodiments, the monitors are capable ofmeasurement at a plurality of time points. In certain embodiments, themonitors are capable of measurement at a plurality of time points ondemand by the user. In certain embodiments, the monitors are capable ofmeasurement at a plurality of time points at user selected times (e.g.,hourly, every eight hours, every twelve hours, daily, etc.). In certainembodiments, the monitors are capable of measurement at a plurality oftime points automatically at regular intervals (e.g., every 5 minutes,every 10 minutes, etc.). In certain embodiments, the monitors arecapable of measurement at a plurality of time points automatically andcontinuously.

In certain embodiments, the monitors measure at a single time point. Incertain embodiments, the monitors measure at a plurality of time points.In certain embodiments, the monitors measure at a plurality of timepoints on demand by the user. In certain embodiments, the monitorsmeasure at a plurality of time points at user selected times (e.g.,hourly, every eight hours, every twelve hours, daily, etc.). In certainembodiments, the monitors measure at a plurality of time pointsautomatically at regular intervals (e.g., every 5 minutes, every 10minutes, etc.). In certain embodiments, the monitors measure at aplurality of time points automatically and continuously.

It will also be apparent to one of skill in the art that clinical markervalues can be stored and used in numerous ways. For example, theclinical marker values can be stored as integrated values, maximum orminimum values, as measurements at specific time intervals, or asinstantaneous readings.

The result displayed by the systems of the invention can be indicativeof status of the subject. In certain embodiments, the result is a numberthat indicates sepsis. For example, the number may be indexed from1-100, wherein “100” indicates sepsis. In certain embodiments, theresult is a “yes/no” signal, wherein “yes” indicates sepsis. In certainembodiments, the result is displayed on a screen. In certainembodiments, the result is transmitted to the medical record of thesubject.

In certain embodiments, the computational device is further capable ofcomparing the amount of lysophosphatidylcholine in the subject to areference amount indicative of the amounts of lysophosphatidylcholine influids or tissues of a plurality of individuals that have, or will have,sepsis. In particular embodiments, the reference amount is the amountmeasured in a SIRS-positive individual 0, 12, 24, 36 or 48 hours priorto onset of sepsis. In certain embodiments, the computational devicecompares the amount of lysophosphatidylcholine in the subject to thereference amount, prior to combining the clinical marker,lysophosphatidylcholine, and/or second biomarker values into a resultindicative of the status of the subject.

6.12 Algorithms for the Advanced Detection of Sepsis

The present invention provides lysophosphatidylcholine, clinicalmarkers, and biomarkers useful for the advanced detection of sepsis in asubject. In this section and the sections that follow, unless specifiedotherwise, the term “markers” refers to the lysophosphatidylcholine,clinical markers, and biomarkers of the invention. Additionally, in thissection and the sections that follow, unless specified otherwise, theterm “marker values” refers to the lysophosphatidylcholine, clinicalmarker and biomarker values made at any given time point for thesubject. These markers and their marker values can be used to detectsepsis in the subject, by determining the probability of sepsis.

The marker values can be used to develop an algorithm, or plurality ofalgorithms, that discriminate between a SIRS subject that will convertto sepsis (“converter”) and a SIRS subject that will not convert tosepsis (“nonconverter”). Typically, a SIRS subject is considered anonconverter when the subject does not develop sepsis in a defined timeperiod (e.g., observation period). This defined time period can be, forexample, twelve hours, twenty four hours, forty-eight hours, a day, aweek, a month, or longer. Specific algorithms that discriminate betweensubjects that develop sepsis and subjects that do not develop sepsisduring a defined period will be described in the subsections below. Oncean algorithm has been built using these exemplary data analysisalgorithms or other techniques known in the art, the algorithm can beused to classify a test subject into one of the two or more phenotypicclasses (e.g., a converter or a nonconverter, SIRS-positive or sepsis).This is accomplished by applying the algorithm to a marker valuesobtained from the test subject. Such algorithms, therefore, have valueas diagnostic indicators.

The present invention provides, in one aspect, for comparison of markervalues from a test subject to marker values obtained from a trainingpopulation. In some embodiments, this comparison is accomplished by (i)fitting an algorithm using the marker values from the trainingpopulation to produce a fitted-algorithm and (ii) applying the algorithmto the marker values from the test subject. As such, the algorithmsapplied in some embodiments of the present invention are used tocalculate the probability of sepsis in a SIRS subject. In preferredembodiments of the invention, the algorithms are used to calculate theprobability of sepsis in a SIRS-positive subject.

In certain embodiments, an the algorithm is any mathematical model knownby those of skill in the art.

In certain embodiments, the algorithm is a statistical model.

In certain embodiments, the algorithm calculates the probability ofsepsis.

In particular embodiments, the probability of sepsis in the subject ismade as a call of “sepsis” for the subject. In particular embodiments,the probability of sepsis in the subject is made as a classification ofthe subject as a “sepsis” subject.

In some embodiments of the present invention, when the results of anapplication of an algorithm indicate that the subject has a highprobability of sepsis, the subject is classified as a “sepsis” subject.If the results of an application of an algorithm indicate that thesubject has a low probability of sepsis, the subject is classified as a“SIRS” subject. Thus, in some embodiments, the result in theabove-described binary decision situation has four possible outcomes:

-   -   (i) truly septic, where the algorithm indicates that the subject        will acquire sepsis and the subject does in fact acquire sepsis        during the definite time period (true positive, TP);    -   (ii) falsely septic, where the algorithm indicates that the        subject will acquire sepsis and the subject, in fact, does not        acquire sepsis during the definite time period (false positive,        FP);    -   (iii) truly SIRS, where the algorithm indicates that the subject        will not acquire sepsis and the subject, in fact, does not        acquire sepsis during the definite time period (true negative,        TN); or    -   (iv) falsely SIRS, where the algorithm indicates that the        subject will not acquire sepsis and the subject, in fact, does        acquire sepsis during the definite time period (false negative,        FN).        It will be appreciated that other definitions for TP, FP, TN, FN        can be made. For example, TP could have been defined as        instances where the algorithm indicates that the subject will        not acquire sepsis and the subject, in fact, does not acquire        sepsis during the definite time period. While all such        alternative definitions are within the scope of the present        invention, for ease of understanding the present invention, the        definitions for TP, FP, TN, and FN given by definitions (i)        through (iv) above will be used herein, unless otherwise stated.

As will be appreciated by those of skill in the art, a number ofquantitative criteria can be used to communicate the performance of thecomparisons made between a test marker values and reference markervalues (e.g., the application of the algorithm to the marker values of atest subject). These include positive predicted value (PPV), negativepredicted value (NPV), specificity, sensitivity, accuracy, andcertainty. In addition, other constructs such a receiver operator curves(ROC) can be used to evaluate algorithm performance. As used herein:

PPV=TP/(TP+FP)

NPV=TN/(TN+FN)

specificity=TN/(TN+FP)

sensitivity=TP/(TP+FN)

accuracy=certainty=(TN+TP)/N

Here, N is the number of samples compared (e.g., the number of testsamples for which a determination of sepsis or SIRS is sought). Forexample, consider the case in which there are ten subjects for whichSIRS/sepsis classification is sought. Marker values are constructed foreach of the ten test subjects. Then, each of the marker values isevaluated by applying an algorithm, where the algorithm was developedbased upon marker values obtained from a training population. In thisexample, N, from the above equations, is equal to 10. Typically, N is anumber of samples, where each sample was collected from a differentmember of a population. This population can, in fact, be of twodifferent types. In one type, the population comprises subjects whosesamples and phenotypic data (e.g., marker values and an indication ofwhether or not the subject acquired sepsis) was used to construct orrefine an algorithm. Such a population is referred to herein as atraining population. In the other type, the population comprisessubjects that were not used to construct the algorithm. Such apopulation is referred to herein as a validation population. Unlessotherwise stated, the population represented by N is either exclusivelya training population or exclusively a validation population, as opposedto a mixture of the two population types. It will be appreciated thatscores such as accuracy will be higher (closer to unity) when they arebased on a training population as opposed to a validation population.Nevertheless, unless otherwise explicitly stated herein, all criteriaused to assess the performance of an algorithm (or other forms ofevaluation of a marker value from a test subject) including certainty(accuracy) refer to criteria that were measured by applying thealgorithm corresponding to the criteria to either a training populationor a validation population. Furthermore, the definitions for PPV, NPV,specificity, sensitivity, and accuracy defined above can also be foundin Draghici, Data Analysis Tools for DNA Microanalysis, 2003, CRC PressLLC, Boca Raton, Fla., pp. 342-343, which is hereby incorporated byreference.

In some embodiments the training population comprises nonconverters andconverters. In some embodiments, marker values are constructed from thispopulation using biological samples collected from the trainingpopulation at some time period prior to the conversion to sepsis by theconverters of the population. As such, for the converters of thetraining population, a biological sample can be collected two weekbefore, one week before, four days before, three days before, one daybefore, or any other time period prior to converters became septic. Inpractice, such collections are obtained by collecting a biologicalsample at regular time intervals after admittance into the hospital witha SIRS diagnosis. For example, in one approach, subjects who have beendiagnosed with SIRS in a hospital are used as a training population.Once admitted to the hospital with SIRS, the biological samples arecollected from the subjects at selected times (e.g., hourly, every eighthours, every twelve hours, daily, etc.). A portion of the subjectsacquire sepsis and a portion of the subjects do not acquire sepsis. Forthe subjects that acquire sepsis, the biological sample taken from thesubjects just prior to the conversion to sepsis are termed the T⁻¹²biological samples. All other biological samples from the subjects areretroactively indexed relative to these biological samples. Forinstance, when a biological sample has been taken from a subject on adaily basis, the biological sample taken the day prior to T⁻¹² sample isreferred to as the T⁻³⁶ biological sample. Time points for biologicalsamples for a nonconverter in the training population are identified by“time-matching” the nonconverter subject with a converter subject. Toillustrate, consider the case in which a subject in the trainingpopulation became clinically-defined as septic on his sixth day ofenrollment. For the sake of illustration, for this subject, T⁻³⁶ is dayfour of the study, and the T⁻³⁶ biological sample is the biologicalsample that was obtained on day four of the study. Likewise, T⁻³⁶ forthe matched nonconverter subject is deemed to be day four of the studyon this paired nonconverter subject.

In some embodiments, N is more than one, more than five, more than ten,more than twenty, between ten and 100, more than 100, or less than 1000subjects. An algorithm (or other forms of comparison) can have at leastabout 99% certainty, or even more, in some embodiments, against atraining population or a validation population. In other embodiments,the certainty is at least about 97%, at least about 95%, at least about90%, at least about 85%, at least about 80%, at least about 75%, or atleast about 70% against a training population or a validationpopulation. The useful degree of certainty may vary, depending on theparticular method of the present invention. As used herein, “certainty”means “accuracy.” In one embodiment, the sensitivity and/or specificityis at is at least about 97%, at least about 95%, at least about 90%, atleast about 85%, at least about 80%, at least about 75%, or at leastabout 70% against a training population or a validation population. Thenumber of marker values that may be used by an algorithm to classify atest subject with adequate certainty is typically about four. Dependingon the degree of certainty sought, however, the number of marker valuesused in an algorithm can be more less, but in all cases is at least two.In one embodiment, the number of marker values that may be used by analgorithm to classify a test subject is optimized to allow aclassification of a test subject with high certainty.

In the examples below, marker data was collected for a plurality ofmarkers in each subject over a time trajectory. That is, for each marker(lysophosphatidylcholine, clinical marker or biomarker), an amount orvalue was measured at a plurality of time points. Algorithms were fittedfrom such marker values from a training population using data analysisalgorithms in order to determine, and predictive accuracy of conversionof the subject from SIRS to sepsis. While new classification andstatistical tools are constantly being developed, the existing body ofpattern recognition and prediction algorithms provide effective dataanalysis algorithms for constructing algorithms. See, for example,National Research Council; Panel on Discriminant Analysis Classificationand Clustering, Discriminant Analysis and Clustering, Washington, D.C.:National Academy Press, which is hereby incorporated by reference.Furthermore, the techniques described in Dudoit et al., 2002,“Comparison of discrimination methods for the classification of tumorsusing gene expression data.” JASA 97; 77-87, hereby incorporated byreference in its entirety, can be used to develop such algorithms.

Relevant statistical models for developing an algorithm include, but arenot limited to, discriminant analysis including linear, logistic, andmore flexible discrimination techniques (see, e.g., Gnanadesikan, 1977,Methods for Statistical Data Analysis of Multivariate Observations, NewYork: Wiley 1977, which is hereby incorporated by reference in itsentirety); tree-based algorithms such as classification and regressiontrees (CART) and variants (see, e.g., Breiman, 1984, Classification andRegression Trees, Belmont, Calif.: Wadsworth International Group, whichis hereby incorporated by reference in its entirety, as well as Section5.1.3, below); generalized additive models (see, e.g., Tibshirani, 1990,Generalized Additive Models, London: Chapman and Hall, which is herebyincorporated by reference in its entirety); and neural networks (see,e.g., Neal, 1996, Bayesian Learning for Neural Networks, New York:Springer-Verlag; Ripley, 1996, Pattern Recognition and Neural Networks,Cambridge University Press; and Insua, 1998, Feedforward neural networksfor nonparametric regression In: Practical Nonparametric andSemiparametric Bayesian Statistics, pp. 181-194, New York: Springer,which is hereby incorporated by reference in its entirety).

In one embodiment, comparison of a test subject's marker value to amarker value obtained from a training population is performed, andcomprises applying an algorithm. The algorithm is constructed using adata analysis algorithm, such as a statistical model. Other suitabledata analysis algorithms for constructing algorithms include, but arenot limited to, linear combinations (see Section 5.12.1, below) andlongitudinal models (see Section 15.12.3, below). The algorithm can bebased upon two, three, four, five, 10, 20 or more features,corresponding to measured observables from one, two, three, four, five,10, 20 or more markers. The algorithm predicts membership within apopulation (or class) with an accuracy of at least about at least about70%, of at least about 75%, of at least about 80%, of at least about85%, of at least about 90%, of at least about 95%, of at least about97%, of at least about 98%, of at least about 99%, or about 100%.

Suitable data analysis algorithms are known in the art. In a someembodiments, the algorithm of the invention comprises LinearCombinations (Section 5.12.1, below), Longitudinal Models (Section5.12.2, below), or Linear Combinations Through Direct Search (Section5.12.3, below). In specific embodiments, the algorithm of the inventionis comprised of Bayes' Theorem (Section 5.12.2.1, below), BayesianStatistical Analysis (Section 5.12.2.1.1, below), or a hidden Markovmodel (HMM; Section 5.12.2.2, below). While such algorithms may be usedto construct an algorithm and/or increase the speed and efficiency ofthe application of the algorithm and to avoid investigator bias, one ofordinary skill in the art will realize that computer-based algorithmsare not required to carry out the methods of the present invention.

6.12.1. Linear Combinations

A logistic regression algorithm provides one approach for combining themarker values into a result indicative of sepsis. The logisticregression algorithm orders the marker values into linear combinationsof marker values, which can then be combined into a single number orindex.

For example, for coefficient vector

$l = \begin{bmatrix}1 \\c_{1} \\c_{2} \\\vdots \\c_{p}\end{bmatrix}$

and vector of marker values,

${x = \begin{bmatrix}1 \\x_{1} \\x_{2} \\\vdots \\x_{p}\end{bmatrix}},$

the linear combination defined by 1 is

Index=l ^(T) x=Σ _(i) c _(i) x _(i).

Here c₀ and x₀=1 allows for an intercept term, i.e., a value for thelinear combination when all the marker values are zero.

Thus, the linear combination allows for the combining of multiple piecesof information, for example, marker values, into a single number orindex, in a simple, smooth way. The coefficients may be thought of asweight terms in the index.

It will be appreciated by those of skill in the art that if a singlemarker is involved, no linear combination is required.

In certain embodiments of the invention, a systemic inflammatorycondition can be detected in a subject if the subject's index surpassesthe threshold (see Section 5.12.5, below) for that condition. Inparticular embodiments of the invention, sepsis can be detected in aSIRS-positive subject if the SIRS-positive subject's index surpasses thesepsis threshold. The threshold can be selected in order to provide abalance between sensitivity and specificity.

It will be appreciated by those of skill in the art that the linearcombination can be developed from training data in several ways. Thetraining data from multiple days and patient could simply be regarded asstatistically independent, and fit to a logistic regression model. Thelogistic regression model fits probabilities of binary outcomes, forexample, a SIRS or sepsis outcome. Specific time points may also beignored, such as the first available time point. Further, estimatedpredicted performance may be used to guide the choice of time points touse. Predictive performance estimates can be obtained bycross-validation (see Section 5.12.4, below), some other re-samplingmethod, or performance on an independent data set, or any other methodknown to those of skill in the art.

It will be appreciated by those of skill in the art that identificationof a linear combination can also be made more directly, avoiding theneed to align time points for SIRS and sepsis training populations, byusing a three-step process. The first step involves development of a setor series of linear combinations which are applied to each subject andeach time point. Application of these linear combinations to thesubject's data generates a sequence of numbers (at a plurality of timepoints) for each subject. The second step involves application of a“functional” to each series, such as a maximum value. The third stepinvolves assessment of the linear combination with regard to how well itseparates SIRS subjects from sepsis subjects. In a preferred embodiment,assessment of the linear combination is made with regard to how well itseparates SIRS-positive subjects from sepsis subjects. Generic measuresof goodness of separation may be determined using Bayesian InformationCriterion or Schwartz Information Criterion (BIC; see, e.g., McQuarrie,A. D. R., and Tsai, C.-L., 1998. Regression and Time Series ModelSelection. World Scientific; and Schwarz, G., 1978, “Estimating thedimension of a model,” Annals of Statistics 6(2):461-464, which arehereby incorporated by reference in their entireties), Area Under theCurve (AUC), or any other method known to those of skill in the art. Thethree-step process if repeated for each linear combination in and themeasures of goodness of separation are compared until the linearcombination is selected that yields the best separation score.

It will be appreciated by those of skill in the art that in practice thesequence of linear combinations can be generated adaptively, as part ofa numerical optimization procedure. In certain embodiments of theinvention, the numerical optimization procedure is an optimizationalgorithm. In particular embodiments, the numerical optimizationprocedure is Simultaneous Perturbation Stochastic Approximation (see,e.g., Spall, J. C. (1999), “Stochastic Optimization: StochasticApproximation and Simulated Annealing,” in Encyclopedia of Electricaland Electronics Engineering (J. G. Webster, ed.), Wiley, New York, vol.20, pp. 529-542, which is hereby incorporated by reference in itsentirety).

It will be appreciated by those of skill in the art that regardless ofhow the linear combination is selected, future statistical performance,as assessed by sensitivity and specificity, can be estimated fromtraining data using cross-validation and other re-sampling approaches.Alternatively, one of skill in the art would understand that futurestatistical performance can be based on a single independent data set.

6.12.1.1 Extending Linear Combinations

It will appreciated by those of skill in the art that extending thelinear combination algorithm can accommodate markers that may notexhibit linear behavior. If information is discrete with two levels,such as gender, an additional marker x_(i) may be used. For example,x_(i) may be set to 1 for cases with one level (say, male) and x_(i) maybe set to 0 for otherwise (female). The marker x_(i) is referred to asan indicator variable. The other level, x_(i)=0 is considered thebaseline. The corresponding coefficient, c_(i), for marker x_(i) willthen be the basis for additional risk accruing to males. Alternatively,if c_(i) is negative, the risk may be decreased. If information isdiscrete with more than two levels, one level can be selectedarbitrarily as a baseline case, and multiple indicators can be used toindicate the level for a given subject. The additional markers may takeon values of 0 and 1, but only one marker can be 1 for any individualsubject.

It will be appreciated by those of skill in the art that a nonlinearmarker can also be derived from linear marker. For example, the markercan be the square of the linear marker value, allowing for a quadraticresponse in that particular marker. The derived marker can also be theslope of a linear trend in marker values calculated from the lastavailable three days, for example, or the change from the firstavailable value or any available value.

It will be appreciated by those of skill in the art that adding anindicator for specific time points provides for model flexibility toadapt to different circumstances. For example, adding an indicator thatis 1 for observations that are within two days of major surgery allowsfor a corresponding coefficient that contributes to the model,effectively raising or lowering the threshold for the affected timepoints.

6.12.1.2 Positive Attributes of Linear Combinations

It will be appreciated by one of skill in the art that linearcombination algorithms can display positive attributes with regard todetecting conversion from SIRS to sepsis in a subject. If marker valuesare distributed in p-dimensional space, then the set of marker valuesgiving the same assay value fall on a plane embedded in this space. Ifan assumption is made that the sepsis and SIRS data clouds areelliptical and similarly-shaped (i.e., having equal covariancematrices), then the optimal boundary is a hyperplane separating thesepsis and SIRS data points. In certain embodiments of the invention,the optimal boundary is a hyperplane separating SIRS and sepsis datapoints. In a preferred embodiment of the invention, the optimal boundaryis a hyperplane separating SIRS-positive and sepsis data points.

6.12.1.3 Alternatives to Linear Combinations

It will be appreciated by those of skill in the art that there arevarious alternatives to linear combinations.

For example, one alternative to linear combinations is to applyindividual thresholds to each marker value, and then fit algorithmsbased on combinations of positive and negative results.

Other alternatives to linear combinations include boundaries developedfrom quadratic discriminant functions, arising when data clouds areelliptical, but having different shapes, or covariance matrices, andboundaries derived from semiparametric classification models such asneural nets, support vector machines, gradient boosting machines, ortree-based classifiers.

A person of skill in the art will appreciate that a complication thatarises when subjects are monitoring at a plurality of time points isthat multiple data points arise from the same subject, but fromdifferent days. The classification of the subject as SIRS-negative,SIRS-positive, septic, etc., could conceivably be based on the subject'strajectories. However, an additional complication is that the subject'strajectories do not have a fixed number of days.

The approaches illustrated in the Examples below address the problem ofmultiple days per subject include a linear logistic regression approachand two related longitudinal approaches. A possible third approach isbased on hidden Markov models (HMMs), which are the current state of theart in voice recognition.

6.12.1.4 Fitting Linear Combinations

6.12.1.4.1 Linear Logistic Regression

Linear logistic regression is one way to estimate a linear combinationfrom data. A linear logistic regression model is of the form

${\ln \left( \frac{p}{1 - p} \right)} = {{c_{0} + {c_{1}x_{1}} + \cdots + {c_{p}x_{p}}} = {l^{T}x}}$

where p is the probability of sepsis. This resembles standard linearregression except it replaces the response with a log ratio. The logratio, called the logistic function, transforms values falling anywhereon the real line to values between zero and one, which can therefore beinterpreted as probabilities. A model is fitted (by maximum likelihood,the current standard in statistics) by taking the observed x values andfinding the coefficients c that make the observed data most likely.

It will be appreciated by those of skill in the art that in the simplestincarnation of the logistic regression approach to the sepsis monitoringproblem, the daily nature of the data, as well as the fact that multipledays arise from the same subject, is ignored. All observations arelabeled as arising from a SIRS subject or from a sepsis subject, and arefit a logistic regression model. While this approach ignores keyvariables, a person of skill in the art would nevertheless recognizethat the approach succeeds in identifying a linear combination thatgives useful marker weights for an index.

6.12.2. Longitudinal Models

The use of longitudinal models provides another approach for combiningthe marker values into a result indicative of sepsis. A longitudinalmodel takes time into account by fitting a function over discreet timepoints. Linear longitudinal models can be evaluated by assuming that thedifferent marker values measured for a subject arise from a lineartrend, on which is superimposed random or Gaussian noise. Consequently,measurements for different markers on the same day for the same subjectcan be correlated. Moreover, linear longitudinal models can also behierarchical. For example, there can be a population-average time trendper marker for sepsis subjects and a population-average time trend permarker for SIRS subjects, giving rise to classification of the linearlongitudinal model as either SIRS or sepsis. Within these twopopulations, time trends per marker may vary from subject to subject.

A person of skill in the art will recognize that varying trajectorylengths complicate longitudinal modeling. For long trajectories, anassumption of steady linear trend becomes less and less reasonable, forexample, if a plateau is reached. Alternatively, a local linear fit to amoving window of points can be used. That is, once a maximum number ofpoints is reached, the oldest point is dropped when a new point ismeasured. The moving window approach is not ideal because the datadropped is not modeled, raising some risk that a phenomenon at earlytime points will not be appropriately accounted for.

6.12.2.1 Class Probabilities Using Bayes' Theorem

It will be appreciated by one of skill in the art that a consequence ofthe different population-average time trends per marker for sepsissubjects versus SIRS subjects is that there is one model for sepsissubjects and another model for SIRS subjects. Therefore, given a datatrajectory from a new subject, classification of the subject as SIRS orsepsis may proceed according to which model better fits the observeddata.

Classification of the subject is made by applying Bayes' Theorem togenerate a group probability derived from the ratio of model likelihoodsand from prevalence and other prior information. For example, in itssimplest form, Bayes' Theorem allows for probability inversion:

${P\left( {Sepsis} \middle| {Data} \right)} = \frac{{P\left( {Data} \middle| {Sepsis} \right)}{P({Sepsis})}}{P({Data})}$

or, more completely,

${P\left( {Sepsis} \middle| {Data} \right)} = \frac{{P\left( {Data} \middle| {Sepsis} \right)}{P({Sepsis})}}{{{P\left( {Data} \middle| {Sepsis} \right)}{P({Sepsis})}} + {{P\left( {Data} \middle| {SIRS} \right)}{P({SIRS})}}}$

where “|” is read “given” or “conditional on.”

It will be appreciated by those of skill in the art that the abovecalculation can be performed in a number of ways. One way is to estimatemodel parameters from available training data, which defines the models

-   -   P(Data|Sepsis)        and    -   P(Data|SIRS)        Then Bayes' theorem can be applied by entering observed new data        into the fitted models. In certain embodiments, the fitted        models are for SIRS and sepsis. In a preferred embodiment, the        fitted models are for SIRS-positive and sepsis.

6.12.2.1.1 Bayesian Statistical Analysis

An improvement on the above approach is to use Bayes' theorem to obtainprobability distributions for the sepsis and SIRS model parameters,which incorporates the uncertainty in the model parameters given thesuperimposed random noise inherent in the training data. This approachis referred to as Bayesian statistical analysis (see, e.g., Lee, PeterM., 1997, Bayesian Statistics: An Introduction, Third Edition, OxfordPress, which is hereby incorporated by reference in its entirety) quiteapart from the use of Bayes' Theorem to make the originalclassification.

In certain embodiments of the invention, a three-step Bayesianstatistical analysis is used to calculate probability of sepsis. First,probably distributions are made for the sepsis and SIRS modelparameters. Second, thousands of random draws of parameters are takenfrom these distributions which give rise to thousands of models, in turngiving rise to thousands of possible values for P(Sepsis|Observed data).Third, the probabilities are averaged to obtain a single probability ofsepsis.

It will be appreciated by those of skill in the art that in practice therandom generation of model parameters is made by Markov chain MonteCarlo methods typically used in Bayesian analysis.

6.12.2.2 Stochastic Process Approaches

As described above, standard longitudinal model methods are not idealfor prediction in the monitoring setting, where trajectory lengths canvary. Alternative approaches are available in the art.

A person of skill in the art will know that one alternative is hiddenMarkov models, a type of discrete stochastic process. A discretestochastic process has a set of time points, typically equally spaced,proceeding into the indefinite future. The process proceeds over thesetime points, taking on a state of being at each time point. The statecould be selected from a finite set of possible states, or could fall ona continuum or even a plane or a higher-dimensional space. When theprocess is at iteration i, its location or state at iteration i+1 isselected randomly, but the probability depends on where the process isand has been. A stochastic process is Markov when the probability itsstate at iteration i+1 depends only on its state at iteration i. Inother words, where it may go on its next step depends only on where itis now; it is memory-less and does not exhibit momentum or othermechanisms which incorporate the past. A random walk is an example of aMarkov stochastic process.

A hidden Markov model (HMM) is a model in which the process takes ondifferent states via a Markov process, yet the states are not themselvesobserved. Rather, data is observed, and each state manifests a differentdistribution of data. Therefore, it is possible to infer the change instate due to the change in the data observed.

Fitting a HMM entails estimating the parameters of the Markov chainprobabilities of transitioning between states, the probabilitydistribution of the initial state, and the data distributionscorresponding to the different states. A determination of the likelynumber of different hidden states may be made by balancing sampling rateor sample number against quality of model fit.

The hidden states are selected to fit the data. For example, one statemay represent infection in a sepsis subject. Another state may representa SIRS-positive state. As a subject converts from one state to another(for example, SIRS-positive to sepsis, or SIRS-positive toSIRS-negative), the conversion may be modeled as the algorithm movingthrough several discrete states.

Identification of sepsis using HMM may be accomplished in several ways.In certain embodiments of the invention, the training data from knownsepsis and SIRS subjects is fit to a single HMM, provided the number ofhidden states and transition probabilities sufficient to accommodateboth populations. By fitting the training data to a single HMM, and thenapplying that fitted HMM to marker value measured at a plurality of timepoints for a test subject, an estimate of the most likely states forthat subject at each time point may be made. This estimate can allow anidentification of those specific states which indicate conversion tosepsis at any particular time point. If any of the identified states atany of the time points are associated with sepsis, and if the states aresufficiently likely, a determination that the subject has converted oris likely to convert to sepsis can be made.

In certain embodiments of the invention, the training data is fit to twoHMMs, one HMM to known sepsis subjects and the other HMM to known SIRSsubjects. Both fitted HMMs can be applied to the marker values measuredat a plurality of time points for the test subject, and a determinationmade as to whether the sepsis HMM explains the subject's time seriesbetter than the SIRS HMM. Probabilities can be developed as with thelongitudinal models described above.

It will be appreciated by those of skill in the art that HMMs may befitted using maximum likelihood methods or with Bayesian methods. In apreferred embodiment of the invention, the Bayesian approach ispreferred. In addition to allowing for uncertainty in the modelparameters, Bayesian models typically can handle many parameters withoutoverfitting, in part by applying shrinkage of parameters to commonvalues.

It will be appreciated by those of skill in the art that aspects of datadistributions, such as variance, for different hidden states may besimilar. Those of skill in the art will understand that optimization ofthe model can be achieved by shrinking variances towards one another, orby enforcing a common variance (while allowing means to shift).

6.12.3. Linear Combinations Through Direct Search

Another alternative to longitudinal models is to search directly for alinear combination that discriminates sepsis from SIRS over entiretrajectories. Numerical search procedures can be used, which generate aseries of candidate linear combinations, l_(m).

It will be appreciated by one of skill in the art that analysis can takea four-step approach. First, given a candidate l_(m),

l _(m) ^(T) x _(ij)

is calculated, where x_(ij) is the data vector for subject j on day i.Second, for each subject j, the maximum x_(i) is taken across days. Thisyields a single number per subject, no matter how many days or how muchdata per day is available. Third, the candidate l_(m) is scoredaccording to how well these numbers separate as sepsis or SIRS. Thescore can be area under the ROC curve or any typical model-comparisonmetric, such as An Information Criterion or Akaike Information Criterion(AIC; see, e.g., Akaike, H., 1974, “A new look at the statistical modelidentification,” IEEE Transactions on Automatic Control 19 (6): 716-723,which is hereby incorporated by reference in its entirety) or BIC, orany other method known to those of skill in the art. Fourth, thecandidate l_(m) which gives the best score is selected.

It will be appreciated by those of skill in the art that taking amaximum is one example of a “functional” applied to trajectories. Themotivation for selecting the maximum is that a physician should takeaction the first time the linear combination exceeds some threshold, soif the maximum is above the threshold, a determination that the subjecthas converted or is likely to convert to sepsis can be made.

Other functionals can also be considered, and are known to those ofskill in the art. For example, since the maximum is prone to outliers,taking the second-highest point may be more robust, while yielding asimilar linear combination. Standard logistic regression approximatelycorresponds to taking the average rather than the maximum.

It will be appreciated by those of skill in the art that many differentnumeric search strategies could be equally applied to the algorithms ofthis invention. In a preferred embodiment, the Simultaneous PerturbationStochastic Approximation is used.

6.12.4. Cross-Validation

Cross-validation is a method of estimating predictive performance thatproceeds by leaving out a fraction of the data, fitting a model,predicting the left-out fraction, and assessing the quality of thatprediction. With regard to classification, for example, the estimate ofpredictive performance can be overall accuracy, likelihood ratio (truepositive rate/false positive rate), area under the receiver operatingcharacteristic (ROC) curve, or area under the ROC curve in theneighborhood of the target specificity.

Cross-validation may be applied to a number of different models and/ormodeling parameters to determine which gives better estimatedperformance, thereby guiding modeling strategy. Once the optimalmodeling strategy is determined, a final model can be fitted with allthe available data.

Preferably, the left-out fraction is small so that the data used fortraining is close in size to the total available training data set. Anoptimal left-out fraction is 10% of the total available training dataset. For example, if there are 100 available data points, it ispreferred that the left-out fraction is be 10%, leaving 90 data pointsfor training.

There are several types of cross-validation known to those of skill inthe art. For example, one common type is “k-fold” cross-validation. Inthis type of cross-validation, the training data set is partitioned intok approximately equal sets. Each set is left out in turn, for kiterations, and the left-out fraction is 1/k. A value of k=10 istypical.

Another common type of cross-validation known to those of skill in theart is the “leave-one-out” cross-validation. As the name implies, thefraction left out is 1/N where N is the number of available trainingdata points. Each observation is left out in turn.

A less common type of cross-validation known to those of skill in theart as the “out-of-bag” cross-validation. In this type ofcross-validation, a randomly-selected set is set aside for oneiteration, then returned, another randomly-selected set is selected forthe next iteration, then returned, and so on. The fraction is the sameacross iterations, and the random selection is independent acrossiterations. There are no constraints on selection from iteration toiteration; a data point could be randomly selected for two iterations ina row, for example, while another data point could be not selected formany iterations. A typically number of iterations used is 400.

6.12.5. Thresholds

Predictive models generate a probability of sepsis. For a single marker,such as lysophosphatidylcholine alone, there is a one-to-onerelationship between the marker value and a sepsis probability, so thethreshold on probabilities may be translated into a threshold onlysophosphatidylcholine. When the model is based on a linear combinationor index of several markers, the threshold on probabilities can betranslated into a threshold on index values.

Typically, thresholds are selected to give the desired trade off betweensensitivity and specificity, as estimated by cross-validation. In oneembodiment of the invention, a threshold is selected for which thespecificity estimate is 90%.

6.12.6. Likelihood Ratio and Day-Specific Likelihood Ratio

Likelihood ratio is a measure of diagnostic utility comprised of [(truepositive rate)/(false positive rate)], or[(sensitivity)/(1−specificity)]. (See, for example, “Statistical Methodsin Diagnostic Medicine,” by Xiao-Hua Zhou, Nancy Obuchowski, and DonnaMcClish, 2002, Wiley, New York). Higher values indicate higher utility.

That the likelihood ratio summarizes the diagnostic information in atest can be seen by a form of Bayes' theorem. In the context ofdiagnosis, Bayes' theorem can be expressed as:

(odds of disease positive given positive test result)=(likelihoodratio)×(odds of disease positive),

where the odds associated with a probability p is p/(1−p). “Odds ofdisease positive” (“prior odds” in the language of Bayes' theorem, asthey are prior to obtaining data) would typically be based uponprevalence, i.e., (prevalence)/(1−prevalence), although it couldalternatively be based on a physician's subjective probability afterviewing a patient. “Odds of disease positive given positive test result”is referred to as “posterior odds” in the language of Bayes' theorem.Bayes' theorem indicates that upon first presentation of the patient,there are prior odds of disease positive status. Once the test result isobserved, the test result moves the prior odds to posterior odds. Themore informative a test, the greater distance it can move prior odds toposterior odds.

In one aspect, estimates are presented as likelihood ratios based onobserved rates. If a false-positive rate is observed to be zero, theestimated likelihood ratio is infinite. This is denoted by themathematical symbol for infinity, “∞.” A likelihood ratio estimate thatis infinity does not indicate a perfect diagnostic system, of course.

In certain embodiments which can involve monitoring patients over anumber of days, a “day-specific likelihood ratio” is one of the moreuseful means for evaluating the status of the patient. The day-specificlikelihood ratio may be viewed in two different ways. To understand thedifference, consider a hypothetical patient who is monitored for fivedays before being diagnosed as septic, and suppose a predictivealgorithm makes a positive call on the third day, but on the fourth andfifth days the algorithm does not make positive calls. In a monitoringregimen in which a doctor would act upon the first positive algorithmcall, this patient would be considered a true positive, even though thepatient was not positive on every day.

One way to construct a day-specific likelihood ratio is to calculatetrue positive rates and false positive rates for each day according tothe regimen. In this case, the hypothetical patient is counted as falsenegative and decreases the likelihood ratio. On days three through fiveit contributes to the true positive rate, and increases the likelihoodratio. This is a “cumulative” likelihood ratio because the patientalgorithm positivity status accumulates. As time progresses to themoment prior to diagnosis, the cumulative likelihood ratio moves toequal the non-day-specific likelihood ratio, i.e.,(sensitivity)/(1−specificity) in which a patient is called positive ifit has been called positive on any day.

That is, the cumulative likelihood ratio evaluated on day k isconstructed by examining all patients evaluable for day k, andcalculating sensitivity and specificity up to day k based on thepresence of any positive algorithm call up to or including day k.

Alternatively, a patient's status on a given day may be evaluated solelyby the algorithm call for that day. This is a “daily snapshot”likelihood ratio.

For illustration, consider a hypothetical patient who is diagnosed asseptic late on the fifth day of monitoring, and suppose the monitoralgorithm yielded positive calls on days three and five. According tothe cumulative regime, the patient would be called negative on days oneand two, and hence would be counted as a false negative, decreasing thecumulative likelihood ratio for those days. On days three, four, andfive, this patient is counted as a true positive, and increases thecumulative likelihood ratio for those days.

Alternatively, for the daily snapshot likelihood ratio, this patient iscounted as a false negative on days one, two, and four, and decreasesthe daily snapshot likelihood ratio for those days. He or she is countedas a true positive on days three and five, and increases the snapshotlikelihood ratio for those days.

Neither day-specific likelihood ratio fully characterizes thealgorithm's performance. Suppose that disease-positive anddisease-negative patient populations become well separated late inpatients' trajectories, yet efforts to exploit this difference lead to ahigh false positive rate early in their trajectories. Then thecumulative likelihood ratio could indicate a relatively low measure ofutility, correctly indicating that the monitoring problem has not yetbeen fully solved, yet masking the fact that there is a basis forimproved performance if the early-trajectory issue can be resolved. Thedaily snapshot likelihood ratio could be very high for days late inpatients' trajectories, correctly identifying differences betweenpatient populations yet masking the fact that the monitoring problem hasnot yet been fully solved. By examining both, one can correctly assessboth actual clinical utility today and potential for improvements in thefuture.

6. EXAMPLES

Examples 1-4 demonstrate the utility of the markers of the invention forthe advance detection of sepsis. Reference marker data sets wereestablished for two populations of patient volunteers.

Patient Populations

Patients were divided into two populations. The first population (“theSIRS group”) represents patients who developed SIRS and who entered intothe present study at “Day 1” but who did not progress to sepsis duringtheir hospital stay. The second population (“the sepsis group”)represents patients who likewise developed SIRS and entered into thepresent study at Day 1 but who progressed to sepsis typically at leastseveral days after entering the study.

Sample Collection

Blood samples were taken about every 24 hours from each study group.Clinical suspicion of sepsis in the sepsis group occurred at “time 0.”The samples were taken at “time-12 hours”, “time-36 hours” and “time-60hours” preceding the day of clinical suspicion of onset of sepsis in thesepsis group. That is, the samples from the sepsis group included thosetaken on the day of entry into the study (Day 1), 60 hours prior toclinical suspicion of sepsis (time-60 hours), 36 hours prior to clinicalsuspicion of sepsis (time-36 hours), and on the day of clinicalsuspicion of onset of sepsis (time-12 hours).

Measurement of Biomarker Amounts

Biomarker amounts were measured used standard quantitative RT-PCRtechniques known to those of skill in the art, which providequantitative measures of biomarker gene expression levels (see, forexample, U.S. Published Application No. 20040106142 A1, the content ofwhich is hereby incorporated in its entirety).

Measurement of Amount of Lysophosphatidylcholine

The amount of lysophosphatidylcholine was measured using standardtechniques known to those of skill in the art (see, for example,Examples 6 and 7 of U.S. patent application Ser. No. 11/541,412, filedSep. 28, 2006, the content of which is hereby incorporated in itsentirety).

6.1 Example 1 Performance as Measured by Out-of-Bag Cross-Validation forLysophosphatidylcholine, Temperature, Respiratory Rate, and OtherBiomarkers as Predictors of Sepsis Using Logistic Regression Algorithms

Examples 1 and 2 are provided that illustrate four data sets and twoalgorithmic approaches. The data sets document lysophosphatidylcholineperformance as a stand alone marker and provide examples of selectingclinical markers or additional biomarkers that improvelysophosphatidylcholine performance as a predictor for sepsis.

Example 1 illustrates “out-of-bag” cross-validation of logisticregression results for lysophophatidylcholine, temperature, respiratoryrate, and other biomarkers. In each example, 400 iterations of“out-of-bag” performance estimation were carried out. That is, for eachof 400 iterations, 10% of the patients were randomly selected betweenthe SIRS and sepsis training populations to be excluded from fitting,then predicted, forming the basis for a performance estimate. Besidesbalance between the two groups, there were no constraints from iterationto iteration; an individual patient could be selected twice in a row,for example. The results of the 400 iterations are averaged for a finalperformance estimate.

Table 1 provides the performance of out-of-bag cross-validation oflogistic regression results using Lysophosphatidylcholine (LPC), ApacheII Temperature (Temp), Apache II Respiratory Rate (RR), Procalcitonin(PCT), C-Reactive Protein (CRP), and Interleukin-6 (IL-6) marker models.In the Examples section, the term “Apache II Temperature,” or “Apache IIRespiratory Rate” indicates that the temperature and respiratory ratewere measured at least every 4 hours, and the maximum value taken overthe 24 hours preceding measurement of the amounts oflysophosphatidylcholine or biomarkers. In the Examples section, unlessspecified otherwise, the term “model” implies a specific algorithmfitted to a specific set of markers from a training set. For example,the “LPC, Temp, RR” model in Table 1 implies an algorithm fitted withLPC, Temp, and RR marker data from a training set.

There were 86 total subjects in the training set, of which 42 were SIRSsubjects and 44 were sepsis subjects. The performance metrics in Table 1represent the cross-validation performance. The data set includes 4 timepoints per subject.

TABLE 1 Performance of LPC, Temp, RR, and other biomarker models using alogistic regression algorithm with out-of-bag cross-validation ModelAccuracy % LPC, Temp, RR 72.66 PCT, Temp, RR 67.69 LPC 61.25 LPC, PCT,CRP 60.62 LPC, PCT 60.03 LPC, IL-6 58.03 PCT 57.81 LPC, CRP 56.25 CRP56.03 IL-6 53.41

As can be seen from Table 1, a combination of the LPC, Temp and RRmarkers provides the highest accuracy model (72.66%) for prediction ofsepsis. From Table 1, it can also be seen that inclusion of Temp and RRmarkers into the LPC model significantly increased the accuracy of themodel from 61.24% to 72.66%.

Table 2 provides the performance of out-of-bag cross-validation oflogistic regression results using Lysophosphatidylcholine (LPC), ApacheII Temperature (Temp), and Apache II Respiratory Rate (RR) markermodels. There were 46 total subjects in the training set, of which 25were SIRS subjects and 21 were sepsis subjects. The performance metricsin Table 2 represent cross-validation performance. The data set included1 time point per subject.

TABLE 2 Performance of LPC, Temp, and RR marker models using a logisticregression algorithm with out-of-bag cross-validation Model Accuracy %Temp 72.56 LPC, Temp, RR 69.75 RR 67.25 LPC 54.81

As can be seen from Table 2, inclusion of Temp and RR markers into theLPC model significantly increased the accuracy from 54.81% to 69.75%.

Table 3 provides the performance of out-of-bag cross-validation oflogistic regression results using Lysophosphatidylcholine (LPC), ApacheII Temperature (Temp), Apache II Respiratory Rate (RR), HLA-DR and“TACH” models. As used herein, “TACH” model means that the markers TIFA(TRAF-interacting protein with a forkhead-associated domain), ARG2(extra-hepatic arginase), CEACAM1 (carcinoembryonic antigen-related celladhesion molecule 1), and HLA-DRA were used to fit the algorithm.

There were 256 total subjects in the test set, of which 116 were SIRSsubjects and 140 were sepsis subjects. The performance metrics in Table3 represent predicted performance of a test data set using logisticmodels fit from the training set (n=90). The data set used multiple timepoints per subject.

TABLE 3 Performance of TACH and other marker models using a logisticregression algorithm with out-of-bag cross-validation Model Accuracy %LPC, TACH 66.41 TACH 64.84 LPC, Temp, RR 61.33 LPC, HLA-DR 60.94 LPC,HLA-DR, Temp 60.55 HLA-DR, Temp, RR 60.16 Temp 59.77 LPC 58.98 HLA-DR,Temp 58.98 Temp, RR 58.20 LPC, HLA-DR, RR 57.03 HLA-DR, RR 55.08 HLA-DR52.34 RR 51.17

As can be seen from Table 3, inclusion of Temp and RR into the LPC modelsignificantly increased the accuracy from 58.98% to 61.33%.

6.2 Example 2 Performance of Lysophosphatidylcholine, Temperature, andRespiratory Rate as Predictors of Sepsis Using Bayesian LongitudinalAlgorithms

Example 2 illustrates Bayesian Longitudinal algorithm results for thelysophosphatidylcholine, temperature and respiratory rate. SIRS andsepsis data were modeled in moving windows of up to 3 days, and assayresults were modeled as a multivariate longitudinal model within thewindow, in which assay results were assumed to vary linearly at aplurality of time points. Predictions for new subjects were madeaccording to their estimated probability of belonging to the sepsispopulation. This probability was calculated by comparing probabilitydensities of the new data for the SIRS and sepsis populations andweighing prevalence, i.e., by Bayes' theorem. Moreover, the parametersof the SIRS and sepsis longitudinal models are themselves estimatedusing Bayesian statistics which yield a “posterior” probabilitydistribution over the unknown model parameters, and the probability ofsepsis is averaged over this posterior distribution.

Classification with Bayesian longitudinal algorithms was computationallyslower than with logistic regression, so predictive performance wasestimated from averaging training data by 3 runs of 10-foldcross-validation. 10-fold cross-validation entails partitioning thepatients into 10 approximately equal groups balanced for sepsis and SIRSrepresentation, excluding in turn each of the 10 groups from fitting,and comparing predictions to observed results. The average performanceover the 10 iterations is the performance estimate.

Table 4 provides the performance of Bayesian longitudinal algorithmresults using Lysophosphatidylcholine (LPC), Apache II Temperature(Temp), and Apache II Respiratory Rate (RR) models. There were 90 totalsubjects in the training set, of which 45 were SIRS subjects and 45 weresepsis subjects. The performance metrics in Table 4 represent theBayesian longitudinal model cross-validation performance. The data setincluded 3 time points per patient.

TABLE 4 Performance of LPC, Temp, and RR models using a Bayesianlongitudinal algorithm Model Accuracy % LPC, Temp, RR 79.5 LPC 72.0

As can be seen from Table 4, inclusion of Temp and RR into the LPC modelsignificantly increased the accuracy from 72.0% to 79.5%.

Further, Examples 1 and 2 illustrate that the choice of algorithm tomodel the marker data does not determine the accuracy of the performanceof that model. For example, regardless of whether a Bayesianlongitudinal algorithm or out-of-bag cross-validation of logisticregression analysis was used, the inclusion of Temp and RR into the LPCmodel was found to significantly increase accuracy. This suggests thatthe methods, monitors, and systems of the invention are algorithmindependent.

6.3 Example 3 Predicted Performance of Lysophosphatidylcholine,Temperature, and Respiratory Rate Markers for the Advanced Detection ofSepsis

Example 3 illustrates that lysophosphatidylcholine, temperature andrespiratory rate can be used for the advanced detection of sepsis in asubject. The example also illustrates that the inclusion of thetemperature and respiratory rate markers significantly increases theaccuracy of the lysophosphatidylcholine model for advanced detection.

In this example, historical models were applied to a test population of257 subjects.

Historical Training and Model Building

Cross-validation performed on training data and each model (LPC, Temp,LPC and Temp, Temp and RR, and LPC, Temp and RR) used an out-of-bag,boot strapping sampling procedure iterated N times (N=400), where ateach iteration logistic regression models were fit and predictedprobabilities computed for samples held back from the model. Then athreshold was selected for each model from the cross-validationprocedure such that accuracy was maximized given specificity greaterthan 90%. Once the thresholds were selected the final logisticregression models were fit using all the training data and thresholdsselected through cross-validation.

The area under the curve (AUC) and ROC curves for the historical modelsare presented in Table 5 and FIG. 2.

TABLE 5 Area under the curve and specific area for various historicalmarker models Model Total Area Spec Area LPC 0.71601 0.02884 RR 0.647710.01937 Temp 0.74484 0.03333 LPC and Temp 0.76956 0.03909 LPC and RR0.73801 0.03166 RR and Temp 0.77397 0.03397 LPC, RR and 0.77985 0.03925Temp

Cross-validation performance from the historical marker modelsconstructed using training data is presented Table 6.

TABLE 6 Performance of various historical marker models Model ThresholdAccuracy (%) Specificity (%) Sensitivity (%) LPC 0.75502 59.45 (49.18,69.28) 90.00 (79.47, 96.79) 28.90 (16.99, 42.71) RR 0.66667 54.62(44.32, 64.71) 90.25 (79.81, 96.93) 19.00 (9.28, 31.51) Temp 0.7228962.35 (52.14, 71.98) 90.50 (80.15, 97.07) 34.20 (21.45, 48.38) LPC andTemp 0.78313 65.62 (55.54, 74.99) 90.00 (79.47, 96.79) 41.25 (27.67,55.62) LPC and RR 0.78313 61.52 (51.29, 71.21) 90.44 (80.06, 97.04)32.60 (20.08, 46.69) RR and Temp 0.75904 61.74 (51.52, 71.42) 90.69(80.4, 97.18) 32.80 (20.25, 46.9) LPC, RR and 0.81526 64.58 (54.45,74.03) 90.25 (79.81, 96.93) 38.90 (25.56, 53.24) Temp

As can be seen from Table 6, inclusion of Temp and RR markers into theLPC model significantly increased the accuracy from 59.45% to 64.58%.

Coefficients used in the historical marker models are presented in Table7.

TABLE 7 Coefficients used in the historical marker models CoefficientsModel (Intercept) LPC Temp RR Threshold LPC 2.51269 −0.03372 0.75502 RR−0.58421 0.02754 0.66667 Temp −36.76293 0.36634 0.72289 LPC and Temp−26.76737 −0.03062 0.28863 0.78313 LPC and RR 1.79624 −0.03400 0.028670.78313 RR and Temp −35.60982 0.34984 0.01983 0.75904 LPC, RR and−25.19826 −0.03102 0.26767 0.02232 0.81526 Temp

Test Performance

The all aforementioned variations of the historical marker models wereapplied to the complete marker data set comprised of 257 subjectsprocessed for test samples and the predicted performance is summarizedin Table 8.

TABLE 8 Predicted performance of test marker models Model ThresholdAccuracy (%) Sensitivity (%) Specificity (%) LPC 0.75502 58.75 44.2976.07 RR 0.66667 47.47 5.71 97.44 Temp 0.72289 59.14 35.00 88.03 LPC andTemp 0.78313 64.59 55.00 76.07 LPC and RR 0.78313 61.48 41.43 85.47 RRand Temp 0.75904 55.25 27.14 88.89 LPC, Temp and RR 0.81526 65.37 47.8686.32

Table 9 shows a calculation of the cumulative likelihood ratio for eachtime point T-324 to T-12, where the T-12 likelihood ratio uses all thedata less than or equal to T-12, and similarly, the T-60 likelihoodratio uses all data time points less than or equal to T-60, etc.

TABLE 9 Cumulative likelihood ratio for each time point T-324 to T-12Time Model −324 −300 −276 −252 −228 −204 −180 −156 −132 −108 −84 −60 −36−12 LPC 0.00 0.00 0.00 1.43 0.39 0.53 0.96 1.42 2.62 1.99 1.91 1.51 1.641.85 RR ∞ 1.58 0.86 1.28 2.20 1.38 1.95 2.23 Temp 0.00 0.00 0.00 0.400.20 0.62 1.08 1.56 1.69 1.74 2.92 LPC and Temp 0.00 0.00 0.00 0.00 0.590.64 1.07 1.23 1.51 1.67 1.81 1.98 2.07 2.30 LPC and RR ∞ 0.79 0.80 3.203.16 4.00 2.70 2.70 2.14 2.30 2.85 RR and Temp 0.00 0.80 1.60 1.05 1.201.14 1.51 1.55 1.82 2.44 LPC, Temp and RR 0.00 0.39 0.40 1.60 1.38 2.062.68 2.84 2.66 2.92 3.50

Using the predicted probabilities from applying the metabolite models tothe test data set, the threshold can be varied to produce ROC curves andmeasure the area under the curve. This gives an indication if adifferent threshold might have better served the test data. The areaunder the curve (AUC) and ROC curves for the historical models arepresented in Table 10 and FIG. 3.

TABLE 10 Area under the curve and specific area for various test markermodels Model Total Area Spec Area LPC 0.60071 0.01696 RR 0.58386 0.01928Temp 0.70657 0.02723 LPC and Temp 0.68235 0.02452 LPC and RR 0.633030.02507 RR and Temp 0.70795 0.02702 LPC, Temp and RR 0.69787 0.03539

Among the sepsis patients who were correctly identified as septic for agiven model, call plots were generated, based on a time normalizationwhere T-12 is the day of onset of sepsis for a sepsis patient and T-12is the last available day for a SIRS patient. Table 11 summaries thenumber of sepsis patients correctly identified as sepsis for each model,the number false positives and the total population of sepsis patients.

TABLE 11 Number of sepsis patients correctly identified as sepsis foreach test marker model Model True Positive False Negative Total LPC 6278 140 RR 8 132 140 Temp 49 91 140 LPC and Temp 77 63 140 LPC and RR 5882 140 RR and Temp 38 102 140 LPC, Temp and RR 67 73 140

FIG. 4 is a graphical representation of the time of call for the testdata according to the model used. Only the true positive calls of Table11 are plotted. The test data is presented as a “box” plot, where theright-hand side of the box corresponds to the 25% lower quantile for thetest data, and the left-hand side of the box corresponds to the 25%upper quantile for the test. The line in the middle of the boxrepresents the median of the test data, whereas the cross (“+”)represents the average of the test data. The x-axis represents timepoints in hours, where T-0 (time zero) represents the moment of onset,T-12 represents 12 hours prior to onset, T-36 represents 36 hours priorto onset, and so forth. The day of onset is encompassed by the 24-hourhour period between T-12 and T12. Consequently, any call up to andincluding the time point T-12 represents a sepsis call at least 12 hoursprior to the moment of onset to sepsis.

As can be seen from FIG. 4, it is possible to uselysophosphatidylcholine, temperature and respiratory rate markers todetect sepsis as early as T-132 or T-108, which represents anywhere fromabout four to five days prior to the day of onset, at T-12.

6.4 Example 4 Predicted Performance of Lysophosphatidylcholine,Temperature, Respiratory Rate, and Additional Biomarkers for theAdvanced Detection of Sepsis

Example 4 illustrates that lysophosphatidylcholine, temperature,respiratory rate, and additional biomarkers can be used for the advanceddetection of sepsis in a subject. The example also illustrates that theinclusion of additional biomarkers can significantly increase theaccuracy of the lysophosphatidylcholine model for advanced detection.

In this example, historical models were applied to a test population of257 subjects.

Historical Training and Model Building

Cross-validation performed on the TACH model, metabolite models, and acombination of the two used an out-of-bag, bootstrap sampling procedureiterated N times (N=400), where at each iteration a logistic regressionmodel was fit and predicted probabilities computed for samples held backfrom the model. Then a threshold was selected from the cross-validationprocedure such that accuracy was maximized given specificity greaterthan 90%. Once the threshold was selected a final logistic regressionmodel was fit using all the training data and threshold selected throughcross-validation.

Area under the curve (AUC) and ROC curves for the historical models arepresented in Table 12 and FIG. 5.

TABLE 12 Area under the curve and specific area for various historicalmarker models Model Total Area Spec Area TACH 0.8203 0.0541 LPC 0.716010.02884 RR 0.64771 0.01937 Temp 0.74484 0.03333 LPC and Temp 0.769560.03909 LPC and RR 0.73801 0.03166 RR and Temp 0.77397 0.03397 LPC, RRand Temp 0.77985 0.03925 LPC and HLA.DRA 0.73512 0.03889 LPC, HLA.DRAand Temp 0.75395 0.04171 LPC, HLA.DRA and RR 0.74506 0.03735

Cross validated training performance for the TACH and metabolite modelsis presented in Table 13.

TABLE 13 Performance of various historical marker models Model Thresh.Accuracy (%) Specificity (%) Sensitivity (%) TACH 0.79518 73.03 (63,81.9) 90.31 (79.15, 97.24) 55.75 (41.07, 69.89) LPC 0.75502 59.45(49.18, 69.28) 90.00 (79.47, 96.79) 28.90 (16.99, 42.71) RR 0.6666754.62 (44.32, 64.71) 90.25 (79.81, 96.93) 19.00 (9.28, 31.51) Temp0.72289 62.35 (52.14, 71.98) 90.50 (80.15, 97.07) 34.20 (21.45, 48.38)LPC and Temp 0.78313 65.62 (55.54, 74.99) 90.00 (79.47, 96.79) 41.25(27.67, 55.62) LPC and RR 0.78313 61.52 (51.29, 71.21) 90.44 (80.06,97.04) 32.60 (20.08, 46.69) RR and Temp 0.75904 61.74 (51.52, 71.42)90.69 (80.4, 97.18) 32.80 (20.25, 46.9) LPC, RR and 0.81526 64.58(54.45, 74.03) 90.25 (79.81, 96.93) 38.90 (25.56, 53.24) Temp LPC andHLA.DRA 0.70683 64.63 (54.50, 74.08) 90.06 (79.56, 96.83) 39.20 (25.83,53.55) LPC, HLA.DRA 0.73896 65.66 (55.58, 75.02) 90.38 (79.98, 97.00)40.95 (27.40, 55.32) and Temp LPC, HLA.DRA 0.79116 63.84 (53.68, 73.35)90.13 (79.62, 96.86) 37.55 (24.37, 51.86) and RR

As can be seen from Table 13, inclusion of biomarker data into the LPCmodel significantly increased the accuracy, from 59.45% to 64.63%, forthe inclusion of HLA.DRA, for example. Inclusion of temperature datafurther increases the accuracy of the LPC, HLA.DRA model to 65.66%.

Coefficients used in the TACH model are presented in Table 14.

TABLE 14 Coefficients used in the TACH model Intercept TIFA.18S ARG2.18SCEACAM1.18S HLA.DRA.18S 19.672 −0.880 −0.394 −0.316 0.628

Coefficients used in the historical marker models combining LPC andclinical markers are presented in Table 15.

TABLE 15 Coefficients used in the historical marker models CoefficientsModel (Intercept) LPC Temp RR Threshold LPC 2.51269 −0.03372 0.75502 RR−0.58421 0.02754 0.66667 Temp −36.76293 0.36634 0.72289 LPC and Temp−26.76737 −0.03062 0.28863 0.78313 LPC and RR 1.79624 −0.03400 0.028670.78313 RR and Temp −35.60982 0.34984 0.01983 0.75904 LPC, RR and−25.19826 −0.03102 0.26767 0.02232 0.81526 Temp

Coefficients used in the historical marker models combining LPC,clinical markers, and the HLA.DRA marker are presented in Table 16.

TABLE 16 Coefficients used in the historical marker models incorporatingHLA.DRA Model (Intercept) LPC HLA.DRA Temp RR Threshold LPC and HLA.DRA−8.95995 −0.02560 0.69793 0.70683 LPC, HLA.DRA and Temp −28.89194−0.02413 0.63354 0.20678 0.73896 LPC, HLA.DRA and RR −9.73960 −0.025800.66111 0.05194 0.79116

Test Performance

The TACH and marker models were applied to common set of 257 subjectsprocessed for test samples and the predicted performance is summarizedin Table 17.

TABLE 17 Predicted performance of test marker models Model Accuracy (%)Specificity (%) Sensitivity (%) TACH 65.63  (59.7, 71.3) 80.17 (72.43,86.83) 53.57  (45.3, 61.73) LPC 58.59 (52.51, 64.54) 75.86 (67.67,83.13) 44.29 (36.19, 52.55) RR 47.27 (41.19, 53.38) 97.41 (93.74, 99.42) 5.71  (2.56, 10.18) Temp 58.98  (52.9, 64.92) 87.93 (81.37, 93.13)35.00 (27.37, 43.08) LPC and Temp 64.45 (58.49, 70.18) 75.86 (67.67,83.13) 55.00 (46.73, 63.12) LPC and RR 61.33 (55.29, 67.18) 85.34(78.33, 91.09) 41.43 (33.44, 49.67) RR and Temp 55.08 (48.96, 61.11)88.79 (82.41, 93.79) 27.14 (20.16, 34.81) LPC, Temp and RR 65.23  (59.3,70.92) 86.21 (79.33, 91.78) 47.86 (39.66, 56.12) LPC and HLA.DRA 60.94(54.89, 66.81) 85.34 (78.33, 91.09) 40.71 (32.76, 48.94) LPC, HLA.DRAand Temp 60.55 (54.49, 66.43) 83.62  (76.34, 89.7) 41.43 (33.44, 49.67)LPC, HLA.DRA and RR 57.03 (50.93, 63.02) 87.07 (80.35, 92.46) 32.14 (24.72, 40.1)

Using the predicted probabilities from applying the models to the testdata set, the threshold can be varied to produce ROC curves and measurethe area under the curve. This gives a indication if a differentthreshold might have better served the test data. The area under thecurve (AUC) and ROC curves for the historical models are presented inTable 18 and FIG. 6.

TABLE 18 Area under the curve and specific area of test marker modelsModel Total Area Spec Area TACH 0.73016 0.02014 LPC 0.59985 0.01679 RR0.58085 0.01918 Temp 0.70624 0.02700 LPC and Temp 0.68140 0.02435 LPCand RR 0.63137 0.02481 RR and Temp 0.70691 0.02674 LPC, Temp and RR0.69647 0.03517 LPC and HLA.DRA 0.64514 0.03097 LPC, HLA.DRA and Temp0.67718 0.02782 LPC, HLA.DRA and RR 0.63686 0.02943

Daily cumulative and daily snapshot likelihood ratios are computed onthe time normalized data. The daily cumulative ratio is calculated byusing all prior time-points for a given patient including the currenttime-point. If a patient is called false positive at anytime prior tocurrent time-point the patient is counted as a false positive regardlessof whether or not the current time-point is a false positive. Table 19presents the daily cumulative and daily snapshot likelihood ratios onthe time normalized data.

TABLE 19 Daily cumulative and daily snapshot likelihood ratios for eachtest model Time Type −324 −300 −276 −252 −228 −204 −180 −156 −132 −108−84 −60 −36 −12 TACH daily 3.00 0.00 0.00 0.65 ∞ 2.45 3.67 2.97 3.656.22 5.16 4.56 cumulative 3.00 1.67 0.39 0.47 1.02 1.57 2.00 2.55 2.613.35 3.11 2.70 LPC daily 0.00 ∞ 0.79 0.83 1.02 4.95 6.22 2.42 1.46 1.613.20 4.08 cumulative 0.00 0.00 0.00 1.43 0.39 0.53 0.96 1.42 2.62 1.991.91 1.51 1.63 1.83 Temp daily 0.00 0.00 0.00 0.77 0.27 0.89 6.90 2.102.72 5.60 7.99 cumulative 0.00 0.00 0.00 0.40 0.20 0.62 1.08 1.56 1.691.73 2.90 RR daily ∞ 0.83 0.00 ∞ ∞ 1.02 ∞ 1.68 cumulative ∞ 1.58 0.861.28 2.20 1.38 1.93 2.21 LPC, Temp daily 0.00 0.00 0.00 1.18 0.83 3.831.65 2.89 4.10 3.66 2.95 5.33 4.20 cumulative 0.00 0.00 0.00 0.00 0.590.64 1.07 1.23 1.51 1.67 1.81 1.98 2.05 2.28 LPC, RR daily ∞ 0.00 ∞ ∞3.30 6.09 2.26 3.92 2.04 3.33 4.90 cumulative 00 0.79 0.80 3.20 3.164.00 2.70 2.70 2.14 2.28 2.83 RR, Temp daily 0.00 0.83 0.77 0.82 0.703.40 1.73 1.70 6.80 5.67 cumulative 0.00 0.80 1.60 1.05 1.20 1.14 1.511.55 1.81 2.42 LPC, Temp, RR daily 0.00 0.39 ∞ ∞ 1.65 2.03 7.22 4.614.33 23.20 6.72 cumulative 0.00 0.39 0.40 1.60 1.38 2.06 2.68 2.84 2.662.90 3.47 LPC, HLA.DRA daily 0.00 0.00 1.50 0.31 0.22 ∞ 2.17 2.05 4.176.40 4.11 7.87 10.93 cumulative 0.00 0.00 1.50 0.83 0.52 0.35 0.55 1.121.47 2.00 2.51 2.68 2.59 2.78 LPC, HLA.DRA, Temp daily 3.00 0.62 0.160.48 2.17 1.64 3.79 7.20 4.11 7.11 9.88 cumulative 3.00 1.67 0.79 0.350.44 0.70 1.29 1.75 2.26 2.39 2.24 2.53 LPC, HLA.DRA, RR daily 0.00 0.00∞ 0.65 ∞ 2.17 2.50 2.22 3.94 2.88 10.47 8.41 cumulative 0.00 0.00 0.000.00 0.79 0.35 0.73 1.16 1.68 1.95 2.44 2.07 2.36 2.49

Table 20 summaries the number of sepsis patients correctly identified assepsis, the number false negatives and the total sepsis population.

TABLE 20 Number of sepsis patients correctly identified as sepsis foreach test model Model False Neg True Pos Total TACH 65 75 140 LPC 78 62140 RR 132 8 140 Temp 91 49 140 LPC and Temp 63 77 140 LPC and RR 82 58140 RR and Temp 102 38 140 LPC, Temp and RR 73 67 140 LPC and HLA.DRA 8357 140 LPC, HLA.DRA and Temp 82 58 140 LPC, HLA.DRA and RR 95 45 140

Table 21 presents the number of SIRS patients called positive and thetotal SIRS population.

TABLE 21 Number of SIRS patients called positive for each test modelModel False Pos True Neg Total TACH 23 93 116 LPC 28 88 116 RR 3 113 116Temp 14 102 116 LPC and Temp 28 88 116 LPC and RR 17 99 116 RR and Temp13 103 116 LPC, Temp and RR 16 100 116 LPC and HLA.DRA 17 99 116 LPC,HLA.DRA and Temp 19 97 116 LPC, HLA.DRA and RR 15 101 116

FIG. 7 is a graphical representation of the time of call for the testdata according to the model used. Only the true positive calls of Table21 are plotted.

As can be seen from FIG. 7, it is possible to uselysophosphatidylcholine and additional biomarkers to detect sepsis asearly as T-132 or T-108, which represents anywhere from about four tofive days prior to the day of onset, at T-12.

6.5 Example 5 Example Calculation of Probability of Sepsis for Two TestSubjects Using the “LPC, Temp, RR” Model

Example 5 presents an example calculation of probability of sepsis fortwo test subjects using the “LPC, Temp, RR” model. Example 5 illustratesthat a calculation of a probability of sepsis can be used for theadvanced detection of sepsis in a subject. The example also illustratesthat a calculation of a probability of sepsis can be used to assess thelikelihood of onset of sepsis, prior to the onset of sepsis

Probability of sepsis, P(Sepsis), was calculated using the LPC, Temp andRR marker values measured over time for the test subject. One testsubject was “SIRS” patient, M107. The other test subject was “sepsis”patient, M163. The SIRS patient, M107, was not diagnosed as septic as ofthe last day of the study (day 13). The sepsis patient, M163, wasdiagnosed as septic on day 8 of the study.

The coefficients for the “LPC, Temp, RR” model were calculated from themarker values for LPC, Temp and RR, as follows:

π=logit(P(Sepsis))=−25.19826+−0.03102*LPC+0.26767*Temp+0.02232*RR,

where P(Sepsis) is the calculated probability of sepsis, and wherelogit(P)=log {P/(1−P)}=log(P)−log(1−P). As seen from the equation, theη=logit(P(Sepsis)) value can be calculated from a linear combination ofthe separate LPC, Temp and RR marker values. The logit(P(Sepsis)) valuetherefore represents an “index” that can be used to calculateprobability of sepsis.

The logit(P(Sepsis)) value was used to calculate probability of sepsisas follows:

${P({Sepsis})} = {{{e\hat{}\left\{ \eta \right\}}/\left( {1 + {e\hat{}\left\{ \eta \right\}}} \right)} = {1 - {1/{\left( {1 + {e\hat{}\left\{ \eta \right\}}} \right).}}}}$

The probability value, P(Sepsis), represents probability of sepsis. Thethreshold for the probability scale was 0.81526. When P(Sepsis) exceededthis threshold (P(Sepsis)>0.81526), sepsis was detected.

Table 22 presents the logit(P(Sepsis)) and P(Sepsis) values calculatedon each day of the study for SIRS patient, M107.

TABLE 22 Logit(P(Sepsis)) and P(Sepsis) values calculated for SIRSpatient, M107 Day LPC RR Temp Truth Logit P(Sepsis) 1 66.15 15 96 SIRS−1.2191 0.2281 2 61.57 16 100 SIRS 0.0160 0.5040 3 64.65 20 101.8 SIRS0.4915 0.6205 4 67.1 12 99.8 SIRS −0.2984 0.4259 6 68.34 24 100 SIRS−0.0155 0.4961 7 67.3 19 101.2 SIRS 0.2264 0.5564 8 70.88 20 101.4 SIRS0.1912 0.5477 9 69.57 18 101.2 SIRS 0.1336 0.5334 10 77.51 18 101.2 SIRS−0.1127 0.4719 11 84.47 20 100 SIRS −0.6051 0.3532 12 66.43 30 100.2SIRS 0.2312 0.5575 13 88.22 30 101.6 SIRS −0.0700 0.4825

According to Table 22, P(Sepsis) for SIRS patient, M163, never exceededthe threshold for detection of sepsis.

Table 23 presents the logit(P(Sepsis)) and P(Sepsis) values calculatedon each day of the study for sepsis patient, M163.

TABLE 23 Logit(P(Sepsis)) and P(Sepsis) values calculated for sepsispatient, M163 Day LPC RR Temp Truth Logit P(Sepsis) 1 37.65 17 101.3Sepsis 1.1282 0.7555 2 34.43 18 101.8 Sepsis 1.3843 0.7997 3 33.88 19101.7 Sepsis 1.3969 0.8017 4 36.45 25 102.7 Sepsis 1.7188 0.8480 5 55.4420 100.3 Sepsis 0.3757 0.5928 6 50.74 29 100.8 Sepsis 0.8562 0.7019 756.01 34 100.2 Sepsis 0.6437 0.6556 8 44.1 37 102.4 Sepsis 1.6690 0.8414

According to Table 23, P(Sepsis) for sepsis patient, M163, exceeded thethreshold for detection of sepsis on day 4 and on day 8.

FIG. 8 presents the marker values (LPC, Temp, RR) of Tables 22 and 23 onthe same plots for comparative purposes. The blue line with circlesrepresents the SIRS patient, M107, whereas the orange line with “+”srepresents the septic patient, M163. The fourth plot presentslogit(P(Sepsis)) values as a linear combination of LPC, Temp and RRmarkers. The grey dashed line on the fourth plot corresponds to athreshold of P(Sepsis)=0.81526 (i.e.,(logit(P(Sepsis))=logit(threshold)=logit(0.81526)=1.484558).

FIG. 9 presents the probability values, P(Sepsis), for probability ofsepsis, calculated from the logit(P(Sepsis) values. The grey dashed linecorresponds to a threshold of P(Sepsis)=0.81526.

As can be seen in FIGS. 8 and 9, the septic patient's index values(represented either by the logit(P(Sepsis)) or P(Sepsis) values) crossedthe threshold twice, once on day 4 and once on day 8. Such datademonstrates successful advanced detection of sepsis in a subject, inthat a positive call for sepsis was made on day 4 or on day 8 of thestudy, prior to the development of clinical manifestations sufficient tosupport a clinical suspicion of sepsis in the patient. In the study, asepsis diagnosis was made for sepsis patient, M163, on day 8, andmonitoring stopped. According to the methods of the invention,intervention on behalf of the patient could be called on day 4, althoughalternative methods such as two above-threshold days in a row or a localaverage index (over some days) could be envisioned. Further, accordingto the methods of the invention, intervention on behalf of the patientcould be called on days 1-3 and 5-7, days for which the septic patient'sindex values had not crossed the threshold, but for which the indexvalues were still significantly elevated relative to the SIRS patient'svalues. Such data therefore demonstrates that assessment of likelihoodof the onset of sepsis can also be made in a subject, prior to the onsetof sepsis.

6.6 Example 6 Example Calculation of Individual Threshold Values of LPC,Temperature and Respiratory Rate Used to Detect Sepsis

Example 6 presents example calculations of individual threshold amountsfor LPC, temperature and respiratory rate markers used to detect sepsis.

As demonstrated in Example 5, when a model is based on a linearcombination of several markers, the threshold on probabilities can betranslated into a threshold on index values. If a test subject exceedsthis threshold index value, sepsis is detected.

For a single marker, such as lysophosphatidylcholine alone, there is aone-to-one relationship between the marker value and a sepsisprobability, such that the threshold on probabilities may be translatedinto a threshold on lysophosphatidylcholine values. Similarly, fortemperature (or respiratory rate) used as a single marker, there also isa one-to-one relationship between the marker value and a sepsisprobability, such that the threshold on probabilities may be translatedinto a threshold on temperature (or respiratory rate) values.

Example Calculation of Lysophosphatidylcholine Threshold Value

The lysophosphosphatidylcholine threshold value corresponds to areference value below which sepsis is detected. Test subject LPC markervalues less than the threshold value indicate sepsis. The examplecalculation of lysophosphosphatidylcholine threshold value uses alogistic regression algorithm. However, it will be apparent to one ofskill in the art that any of the algorithms discussed above can be usedto calculate threshold values for the individual markers of theinvention.

The equation for the logistic regression “LPC” model using thecoefficient and threshold values of Table 7 is:

logit(P(Sepsis))=2.51269−0.03372×LPC.

The inverse of the logistic function generates a probability for sepsis:

P(Sepsis)=1/(1+ê{2.51269−0.03372×LPC}).

The threshold on the probability scale (0.75502) can be translated intoa direct threshold on LPC as follows:

logit(0.75502)=1.125568=2.51269−0.03372×LPC.

Solving for LPC yields:

LPC=−(1.125568−2.51269)/0.03372=41.13648.

Thus, the LPC threshold is 41.13648 units, which translates intoapproximately 41.14 μM. According to the model, sepsis is detected in atest subject with a LPC marker value less than 41.14 μM.

Example Calculation of Temperature and Respiratory Rate Threshold Values

Similarly, the equations for the logistic regression “Temp” and “RR”model using the coefficient and threshold values of Table 7 are:

logit(0.72289)=−36.76293+0.36634*Temp,

and

logit(0.66667)=−0.58421+0.02754*RR,

respectively.

Using the inverse of the logistic functions to generate probabilitiesfor sepsis, and translating the thresholds on the probability scales(0.722889 and 0.66667, respectively) into direct thresholds on Temp andRR, yields threshold values of Temp=39.43° C., and RR=46.38 breaths perminute. According to the Temp model, sepsis is detected in a testsubject with a Temp marker value greater than 39.43° C. According to theRR model, sepsis is detected in a test subject with a RR marker valuegreater than 46.38 breaths per minute.

All publications and patent applications cited in this specification areherein incorporated by reference as if each individual publication orpatent application were specifically and individually indicated to beincorporated by reference. Although the foregoing invention has beendescribed in some detail by way of illustration and example for purposesof clarity of understanding, it will be readily apparent to those ofordinary skill in the art in light of the teachings of this inventionthat certain changes and modifications may be made thereto withoutdeparting from the spirit or scope of the appended claims.

1. A method for the advanced detection of sepsis in a subject,comprising the steps of: (a) measuring at a plurality of time points anamount of lysophosphatidylcholine in fluid or tissue of the subject; and(b) measuring at a plurality of time points one or more clinical markersof the subject, to detect sepsis in the subject. 2-84. (canceled)